blockchain and cyber security research paper

Cybersecurity, Data Privacy and Blockchain: A Review

• Review Article
• Open access
• Published: 12 January 2022
• Volume 3 , article number  127 , ( 2022 )

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• Vinden Wylde 1 ,
• Nisha Rawindaran 1 ,
• John Lawrence 1 ,
• Rushil Balasubramanian 1 ,
• Edmond Prakash   ORCID: orcid.org/0000-0001-9129-0186 1 ,
• Ambikesh Jayal 2 ,
• Imtiaz Khan 1 ,
• Chaminda Hewage 1 &
• Jon Platts 1  

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In this paper, we identify and review key challenges to bridge the knowledge-gap between SME’s, companies, organisations, businesses, government institutions and the general public in adopting, promoting and utilising Blockchain technology. The challenges indicated are Cybersecurity and Data privacy in this instance. Additional challenges are set out supported by literature, in researching data security management systems and legal frameworks to ascertaining the types and varieties of valid encryption, data acquisition, policy and outcomes under ISO 27001 and the General Data Protection Regulations. Blockchain, a revolutionary method of storage and immutability, provides a robust storage strategy, and when coupled with a Smart Contract, gives users the ability to form partnerships, share information and consent via a legally-based system of carrying out business transactions in a secure digital domain. Globally, ethical and legal challenges significantly differ; consent and trust in the public and private sectors in deploying such defensive data management strategies, is directly related to the accountability and transparency systems in place to deliver certainty and justice. Therefore, investment and research in these areas is crucial to establishing a dialogue between nations to include health, finance and market strategies that should encompass all levels of society. A framework is proposed with elements to include Big Data, Machine Learning and Visualisation methods and techniques. Through the literature we identify a system necessary in carrying out experiments to detect, capture, process and store data. This includes isolating packet data to inform levels of Cybersecurity and privacy-related activities, and ensuring transparency demonstrated in a secure, smart and effective manner.

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Introduction

With the recent emphasis on societies in increasing their dependency on cloud technologies, coupled with the human need to communicate and share data via digital networks, Internet of Things (IoT) devices to include smart phones, industrial and domestic appliances, continue to be a necessary function in conducting business. Social exchanges and transactional types of data for example, drive the financial markets thus facilitating in the swift development of emerging technologies at an ever faster rate to keep up with supply and demand trends. In a domestic setting, the sharing of digital media (videos, music, pictures, documents (data)) through messaging services to enhance subject areas such as information technology, sport, social sciences, education and health for example, IoT devices enable the efficient and effective transfer of data world-wide instantly via the Internet of Everything (IoE) via the cloud. In an industrial context, Smart Sensors, Application Programming Interfaces (API) and IoT networks facilitate remote working across digital boundaries globally.

These potentially devastating instances of data sharing and/or criminality, influence the confidentiality and protections set out by governments, businesses and organisations, culminating in legal and ethical disputes with significant financial ramifications due to Denial of Service (DDoS) attacks for example, that would damage and disrupt entire business data architectures, infrastructures networks and services on a large scale. Consequently, with society relying more and more on the exchange and processing of Personal Identifiable Information (PII) via IoT, trust in renowned institutions and government organisations to include broadcast and digital media outlets becomes a main issue. As a user chooses to share social network, personal and confidential information whilst shopping on-line for example, they should be aware of the nature and intent of cyber-criminality and have faith in the criminal justice system of a given territory.

On the other hand, for businesses, organisations, government bodies and academic institutions to be able to freely validate and authenticate their data in the service of societies globally, Artificial Intelligence (AI), Big Data (BD), Blockchain (BC) Combined Technologies and methodologies, contribute significantly in mitigating cyber-crime, whilst providing legal bodies the power to hold companies, organisation and institutions to account. One such method is the Smart Contract (SC) for example, and when utilised in the drafting and consenting of a legal document or digital certificate, provides an evidence-based transparent method in enhancing the legal credibility and value of a financial transaction. As a function of BC, the SC is validated, implemented then shared across a Pier-to-Pier (P2P) network as a Distributed Ledger Technology (DLT) for all parties to see which provides transparency and accountability.

• Cybersecurity

When utilising elements of cybersecurity, these technical requirements facilitate in the effective management of IoT hardware and software operations, physical interfaces and internal policy development. Additionally, the management system ISO 27001 supports network communication protocols, data access control and cryptography (i.e., password encryption), that contribute in ensuring a robust and secure communication method inclusive of cybersecurity staff training; all whilst minimising network communication attacks in the presence of malicious third-parties [ 1 ].

However, to harness and derive value from the volume, variety and veracity of data available, concepts such as BD, AI and Machine Learning (ML) utilise prescribed algorithms and analysis techniques across vast quantities of public, private and sensitive data through digital networks, that exponentially increases the risk of data breaches, viruses and malicious attacks. In other words, in successfully utilising these technologies in the legal acquisition and processing of data from the public and private sectors, also to include practical user measures, potentially reveals challenges and vulnerabilities that can further expose a user or group to cyber-criminality.

Data Privacy

Additionally, the ISO 27001 framework functions in conjunction with the General Data Protection Regulation (GDPR) Regulation (EU) 2016/679, and Data Protection Act 2018 c. 12 (DPA), in facilitating personal data controls and measures in the UK and European Unions (EU) digital boundaries. In processing medical data for example, a mandatory Data Protection Impact Assessment (DPIA) is undertaken in identifying and establishing the risks alongside eight core principles to include; lawful and ethical methods of data acquisition, data storage security and duration, fair use, and for data to be kept within specified locations and regions [ 2 ].

In utilising these legal frameworks and management systems, tracking tools such as ‘cookies’ for example may utilise the aforementioned AI, ML and algorithmic analysis unlawfully, and as a result, a user may not be aware of the tracking nature and capabilities contained within the software for analysis and marketing purposes. Additionally, without user consent, the awareness and continual levels of maintenance required of said cookies, that are a necessary function in surfing the web, could expose business networks to anti-forensic methods, legal jurisdiction matters, system hardware and Service Level Agreement (SLA) breaches, which compound over time and further aggravate technical, legal and ethical challenges in operating IoT devices in a compliant, safe and secure business environment.

Furthermore, when utilising in a healthcare service context, a SC policy with cryptography as a cybersecurity control method, gives transparency, protected agency and responsibility to the public, financial markets, business professionals and legal representatives, in conducting valid and transparent actions or investigations on behalf of the directorate or client. When this method is applied retrospectively, it also gives accountability in upholding vigilance and resilience when managing cyberspace, an operators duty of care and consideration of confidential data breaches, its sharing, and ramifications of exposing vast amounts of confidential National Health Service (NHS) patient data for example [ 3 ].

Blockchain Security

BC based functions, methods and systems utilise concepts like Cryptocurrency (i.e., Bitcoin and Etherium) as an alternative to fiat currencies, representative consensus protocols, anonymous signatures, off-chain storage and non-interactive zero-knowledge proofs. These concepts provide validity, anonymity, and transparency when coupled with inner corporate or organisational audit, policy deployment, healthcare provider and security service function of carrying out legal and domestic activities. This system is trustless by design and offers promise for equitable and transparent transactions.

As per all the above, this review and study proposes an intelligent framework to aide in the identification and detection of compromised network packet data. The use of BC and SC are to be utilised as an information carrier (data) and for evaluation, validation and testing with pre-prescribed control protocols. Then, to conduct a literature review in ascertaining current methodologies, techniques and protocols in aiding the development of said framework. To minimise human intervention, an intelligent automated approach is utilised in the capturing of network data at pre-determined intervals. Ultimately, the data events are tested against a framework with analysis of findings to demonstrate comprehensive framework feasibility (see Fig.  1 ).

Cybersecurity refers to: “a measure for protecting computer systems, networks, and information from disruption or unauthorized access, use, disclosure, modification or destruction” [ 4 ]. Therefore, in trying to understand cybersecurity and its applications towards IoT and smart devices, brings additional questions that need analysis through various notions of cyberspace. One solution is unifying all the terminologies above to bring together the importance of understanding where network intrusion comes from, how it is detected, and how prevention of cyber threats occur. When looking at prevention, AI and ML uses could also potentially contribute to the rise in using this technology to secure and protect data [ 5 ].

Cybersecurity IoT and ML

As Information Technology (IT) facilities expanded, overall digital technology saw growth in more devices being introduced and connected to the internet, so that access to data is freely available to allow for more activities to be undertaken. These activities allow for outcomes to be predicted [ 6 ]. Therefore, in response, various ML mathematical algorithms allow for classification usage such as Support Vector Machines (SVM), Decision Trees and Neural Networks. These algorithms all compound and highlight how data is treated and managed to produce an outcome, and predictability that is required to contribute to economic growth as societies move forward. ML capabilities go far beyond the expectations of conquering human hobbies, but lends further into everyday chores and events in daily lives.

Other real-life examples of ML usage rest in many industries focusing on identifying fake news, implementation of spam filters, identifying fraudulent or criminal activities online, and improving marketing campaigns. These large quantities of data are often private and sensitive, whilst travelling through Cyberspace transferring data along the way. Disadvantageously, this existence of cyberspace creates a wider security attack surface for potential malicious activities to occur. This demonstrates that human factors and the large influence it has on the security of IoT [ 7 ] is highly impactful.

Humans’ perceptions of security and privacy concerning these devices are also a subject to be discussed, for example, the concept of ‘Cookies’ as a tracking tool for online web surfing, and its safety measures, which are often shoehorned as a debate in itself, and the awareness of how it should be used has been seen through glazed eyes [ 8 ]. However, recent reports suggest that many contributory questions arise from understanding IoT and the safety net around it, and how humans cope and live alongside IoT. Anti-forensic methods, jurisdiction and Service Level Agreements (SLA) for example, all further aggravate technical, privacy, security, and legal challenges. In addition, the presence of GDPR and IoT, coupled with the human factors involved, present immense challenges in keeping these devices safe and secure.

Cybersecurity and SMEs

UK Small to Medium Enterprises (SME’s) have always seen challenges in understanding cybersecurity due to the increase in threats that have risen in recent years. The European Commission’s employment criterion for an SME minimum cyber-criterion is that for any business that employs less than 250 people [ 9 ]. The challenges faced are both operational and commercial in SMEs using Intrusion Detection mechanisms coupled together with AI and ML techniques in the protection of their data.

SMEs intrusion, detection, and prevention methods has become a priority in the realisation of keeping their data secure and safe with the integration of real-world objects and IoT, with understanding how ML techniques and AI can help secure zero-day attacks. Rawindaran et al. [ 1 ] took particular interest in the SME market and showcased an experimental scenario in which the intrusion, detection and prevention models were compared, and the views of the SME examined. The study looked at the various approaches in identifying ways to detect and protect any intrusions coming into the network and what operating devices would help in this process. The paper also explored the understanding in trying to protect the data and how government policies and procedures such as GDPR in the UK/EU, could assist towards this process [ 10 ].

Cybersecurity and SME Attacks

Rawindaran et al. [ 11 ] further examined the impact of how threat levels of attacks such as Ransomware, Phishing, Malware, and Social-engineering amongst others, were compared between an Open-Source device, such as SNORT and pfSense, and Commercial Network Intrusion Detection (NIDs) such as Cisco. There were three different NIDs and their features were compared. It was concluded that whilst SNORT and pfSense were free to use from the Open-Source market, it required a certain level of expertise to implement and embed the rules into a business solution. It was also noted that Cisco, due to their engineering expertise and their position as market leaders in the industry, were able to embed these free rules and use it to their advantage.

What emerged from this study was how businesses and organisations with the help of government policies and processes, needed to work together to combat these hackers, malicious actors, and their bots, and manage and stay ahead of the game [ 4 ]. The paper also discussed various ML approaches such as signature based models and anomaly based rules used by these devices to combat these attacks [ 12 ].

Additionally, signature based models could only detect attacks that were known, whereas anomaly-based systems were able to detect unknown attacks [ 13 ]. Anomaly-based NIDs made it possible to detect attacks whose signatures were not included in rule files. Unfortunately, due to the maturity of Anomaly NIDs, the costs were still very high to run and required computing power that were unrealistic in the SME environment. Anomaly based NIDs whilst still in its infancy, require a deeper analysis and future study.

Rawindaran’s study provided perspectives on better comparisons and relative conclusions and how it was important to explore further both the empirical as well as in scenario analysis for different dimensions, the nature and context of cyber security in the current world of internet and cyber connections. Rawindaran also explored how ML techniques have become vital in the growth and dependencies of these SMEs in the UK in their operations and commercial environment. This study took on an initial look at success stories from big technology companies such as Amazon, Google, and Facebook, in their use of ML techniques for their cybersecurity [ 14 ]. The methodology adopted in this study focused on structured survey questions on a selected sample number of respondents and directed its questions to the SMEs management, technical and non-technical professionals.

Cybersecurity and ML to Mitigate Attacks

Rawindaran et al., found that awareness of ML and its uses is still on a learning curve and has yet to be defined. The study brought to surface the three main categories of ML that being Supervised Learning, Unsupervised Learning and Reinforcement Learning and the algorithms that sit behind them [ 15 ]. Examples of Supervised Learning included real life predictive text in tweets in Twitter and product reviews in Amazon and eBay, calculating temperature, insurance premiums, pricing, and number of workers to the revenue of a business.

Examples of Unsupervised Learning include examples include identifying fake news, implementation of spam filter, identifying fraudulent or criminal activity online, and marketing campaigns. Reinforcement Learning shows example of playing a video game that provides a reward system when the algorithm takes an action. Each learning method used algorithms that helped with calculations and predictions and a dataset that helped in the development and structures of its uses. It also deducted and quantified examples and showed strength in the SMEs perception and awareness towards ML and its uses.

The methods of ML and its algorithms lead into the focus of this study in which SMEs were given the opportunity to make themselves aware of these algorithms that exist within their own cybersecurity software package. Further the analysis of this study showed the existence of these algorithms such as Neural Networks, Support Vector Machines, Deep Networks and Bayesian, however most of these were cleverly embedded within the software used [ 16 ].

The initial idea of using an Intrusion, Detection and Prevention System (IDPS) method, from either a commercial or Open-Source device to protect the data of the SME, comes with the knowledge of ML and AI. As hackers become increasingly clever and the uses of bots take over, their ‘attacking’ methods, as protectors of the systems, society has had to lean on ML and AI technology to help. An IDPS system is able to help through the use of ML, to learn about malicious patterns compared to valid patterns on the internet. These various approaches are needed to protect and shield data. ML through anomaly detection, proved to be more effective in its zero-day detection than that of signature based in its effectiveness towards cybersecurity and adoption within the UK SMEs. There is a significant gap that needs to be fulfilled by perhaps more variations in the devices used for SMEs such as opensource and voluntary participants from knowledge of the community to keep future proofing these devices.

Cybersecurity and Adversarial ML

With the increased use of ML in Intrusion Detection Systems (IDS) and IDPS systems within cyber security packages of SME communities, there suddenly lies the introduction of a new type of attack called Adversarial Machine Learning (AML) [ 1 ]. In a paper by Anthi et al. [ 17 ] states that with the introduction of ML IDSs, comes the creation of additional attack vectors specifically trying to break the ML algorithms and causing a bypass to these IDS and IDPS systems. This causes the learning models of ML algorithms subject to cyber-attacks, often referred to as AML.

These AMLs are thought to be detrimental as they can cause further delayed attack detection which could result in infrastructure damages, financial loss, and even loss of life. As [ 17 ] suggests, the emergence of Industrial Control Systems (ICS) plays a critical part on national infrastructure such as manufacturing, power/smart grids, water treatment plants, gas and oil refineries, and health-care. With ICS becoming more integrated and connected to the internet, the degree of remote access and monitoring functionalities increases thus becoming a vulnerable point target for cyber war. Additionally, with ICS more prone to targeted attacks, new IDS systems have been used to cater for the niche market of ICS, thus introducing vulnerabilities in particular to the training model of ML.

With the introduction of these new IDSs, has also introduced new attack vectors into the mix. The definition of AML provided by Anthi states that: “The act of deploying attacks towards machine learning-based systems is known as Adversarial Machine Learning (AML) and its aim is to exploit the weaknesses of the pre-trained model which has ’blind spots’ between data points it has seen during training”.

This is challenging as ML usage in IDS is becoming a tool used in daily attack detection. The study showed how AML is used to target supervised models by generating adversarial samples and exploring and penetrating classification behaviours. This was utilised by the use of authentic power system datasets to train and test supervised machine learning classifiers through its vulnerabilities. The two popular methods that were used in AML testing were automatically generated perturbed samples that were the Fast Gradient Sign Method (FGSM) and the Jacobian based Saliency Map Attack (JSMA).

Both methods showed how AML was used in penetration of systems through ML training models leading onto cyber-attacks. In another study by Catak et al. [ 18 ], further explored the security problems associated with AML, this time through the networks of 6G applications in communicative technology, that focused on deep learning methods and training. With the rapid development and growth of deep learning and its algorithms in the future technology pipeline of 6G was to further understand the security concerns around it.

Cataks’ paper [ 18 ] produced faulty results through manipulation of deep learning models for 6G applications to understand AML attacks using Millimetre Wave (mmWave) beam prediction in this case. AML mitigation and preventative methods were also used to try and stop these attacks from occurring for 6G security in mmWave beam prediction application with fast gradient sign method attack. In conclusion to Cataks’ paper found that several iterations of introducing faulty results gave a more secure outcome of the performance and security of the device. ML deep learning methods and algorithms were able to use these faulty results in altering the adversarial training approach. This increased the RF beam-forming prediction performance and created a more accurate predictor in identifying these attacks against the ML applications use.

Cybersecurity: Summary

As with any new technology that stems to improve the cyber highways in lessening the effects of cyber-attacks, it is always coupled by the counterattack measure within this space. Being aware of these adversaries and future research will help reduce, or at least control the level of attacks being present in any cyberspace and landscape moving forward. The recognition of funding gaps that could be fulfilled by the government to support SMEs in the form of grants, subsidies, and similar financial assistance, through various public sector policies is also an important route to consider. Awareness and training for all SME management and their staff is important to understand the basic and perhaps advanced appreciation of cybersecurity through the eyes of ML and AI.

Whilst technology giants might lead the path in its implementation of ML and cybersecurity through its many variations of intrusion, detection, and prevention methods, it is these firms that will set precedence and bring awareness down to a SME level and the importance of ML in keeping our cyber world safe. Understanding whilst ML is increasing in usage through IDS and IDPS systems to reduce the cyber attack footprint, means that the rise in AML also is something to be concerned about.

An example in GDPR Recital 4 and in the proceeding Directive 1995/46/EC Recital 2, a main objective “the processing of personal data should be designed to serve mankind”. For this purpose, the Data Controller ensures legal compliance and legal justification of data processing out of necessity (not only processing convenience) and proportionality. For the acquisition of high-risk health data for example, GDPR mandates that a DPIA is carried out to mitigate risk and assess risk level to include if the data should be processed or not [ 19 ]. With data protection law, the UK and EU demonstrate cooperation, ethics, transparency with robust control methods in mitigating data privacy breaches. However, this also brings attention to the range of legal frameworks and the general movement of people globally. This should inform governments and business in data protection strategies.

Data Privacy: Legal Frameworks [UK-EU]

Between the UK and EU, the Data Protection Act 2018 (DPA) and General Data Protection Regulations 2016 (GDPR) function together in overseeing how businesses, organisations and governments, utilise personal data. Eight key objectives guide anyone responsible for the handing and processing of personal data, and strictly imposes that data has to be lawful [acquisition], fair, accurate and up-to-date, not kept longer than needed, kept safe and secure, and not to be transferred outside the European Economic Area (EEA). By design, GDPR encompasses human rights with additional data collecting and processing principles (e.g. purpose, data-types and processing duration) [ 20 ].

Data Privacy: SARS-Cov-2: Covid-19

In supporting the effort in mitigating disease transmission from the coronavirus pandemic (Covid-19), the cloud, cell-networks and IoT devices such as smart-phones, sensors and domestic appliances, continue to play a vital role in a wide range of global Tracing-Testing-Tracking programs. Many different approaches are adopted by global communities in minimising person-to-person transmission [ 21 , 22 ]. This demonstrates that in response to the pandemic, coupled with the urgency in developing and deploying digital solutions, data privacy implications become ever more challenging with increasing data privacy risks. As a result, the handling of personal data [acquisition] research has developed and expanded [ 23 ].

However, in mitigating data privacy risks under adverse social and environmental conditions, it is not simply a matter of deploying digital solutions. The challenges presented in terms of service delivery (consistency, proportionality and transparency), also potentially increases the risk of data privacy breaches. Therefore, in terms of scalability via the cloud, partnerships between populations, businesses and governments could harmonise policy development and implementation with digital solutions.

Data Privacy: Consent—Contact Tracing Apps

In a Republic of Ireland survey conducted with over 8000 participants, it was found that 54% would accept using a contact tracing app. Similarly, in the UK from a survey of 2000 participants found that 55% would accept using a government-controlled app, with higher uptake specifically for the NHS contact tracing app [ 21 ]. This information demonstrates a lack of app uptake in the remaining 45% of the British population that could undermine a governments ability in effectively handling data collection and the processing of critical medical information.

In contrast, other countries infer citizen consent when data collection is initiated for the public good. Meaning that private parties’ access to data is also endorsed by governments. Amnesty International (2020) also brings attention to many instances of questionable data privacy practices throughout numerous countries [ 21 ]. The examples potentially show the scale of data protection perceptions and attitudes and how they are interpreted, thus justifying a more focused and intensive approach to data privacy collaborative research. By analysing a variety of legal and regulatory frameworks, solutions and practices in a pandemic or crisis situation, we can learn how to effectively apply powerful and scalable outcomes. For example, robust and transparent data is necessary for the urgently needed Covid-19 vaccine distribution efforts for each nation [ 24 ].

Transparency: NHS Test-Trace App

In response to the pandemic, the UK Government and NHS X (Digital) contact tracing app, aided by the private sector, brought into question their overall GDPR utility and compliance. Sub-contractors and companies that represent NHS X are also considered as processors of data, which bring additional GDPR compliance pressures. In this instance, the NHS X app code and DPIA was voluntarily submitted to the Information Commissioners Office (ICO) without the data store. This potentially highlights a lack of transparency with GDPR compliance, health surveillance capabilities and data storage capacities. The Joint Committee on Human Rights (JCHR) for example, were concerned at the rapid development and deployment of the contact tracing app in March 2020 [ 19 ].

Data Storage and Identification

Clear definitions and solutions are needed for data and storage methods. Currently, obtaining an integrated and comprehensive view of (1) internal organisational personal data storage, (2) full organisational content comprehension of regulation, and (3) an auditable trail of necessary data processing activities [ 20 ]. Although GDPR compliance has significantly enhanced personal data protection (e.g. PII, PII sharing via add and marketing, collecting and sharing location data, child PII sharing, law enforcement, and data aggregation), more research is needed in facilitating a users right to erasure, to update and delete data and to completely satisfy the GDPR promise [ 25 ].

Accountability and Traceability: BC & SC

To aide government transparency and societal trust, part of a solution is robust data privacy and accountability policies. Antal et al., discusses how BC can be effective in traceability, transparency, vaccine ID, assurances of it’s delivery, storage to include self-reporting of side effects. The authors implement a BC strategy using the inherent integrity and immutability of BC with ’in case of beneficiary registration for vaccination’ provision, thus eliminating identity impersonations and identity theft [ 26 ].

An example from Honduras demonstrates how a Toronto-based technology launched ’Civitas’, with user and government linked ID on a BC-based network. The BC contains the necessary data for determining when an individual can buy medicine, go food shopping, and also data to inform government agencies in resource and deployment strategies [ 27 ]. The GDPR for example, would conflict with this contact tracing methodology. More specifically, the right for a user to be forgotten (Article 17: Right to Erasure) due to BC immutability, and processing speed that would also inhibit BC network uptake and scalability.

However, BC in this case could operate within the confines of management and governance of BD repositories and warehouses whilst leveraging SC to enhance accountability, transparency and consistency in the appropriate forum.

Trust: Vaccine Hesitancy in UK Households

Whilst a global effort was underway in mass vaccination programs, the UK strategy highlighted disparities from a lack of public engagement between public health bodies and ethnic minorities from historic mistrust and a lack of understanding in technology [ 24 , 28 ]. Additional hesitancy included acute and chronic health effects from the vaccine.

A UK survey from 2020 for example, illustrated how Black, Asian, Minorities and Ethnic (BAME) communities had high vaccine hesitancy rates, when compared to white ethnic populations [ 28 ]. In Robertson 2021, the authors state that “Herd immunity may be achievable through vaccination in the UK but a focus on specific ethnic minority and socioeconomic groups is needed to ensure an equitable vaccination program” [ 29 ]. Including a more targeted approach to mental illness and disability [ 30 ].

Data Privacy—Summary

In a global setting, is it possible to ethically and accurately collect data [also without consent] whilst also providing legibility for effective data collection, resource allocation and deployment strategies? A small part of the solution is in gaining a populations’ trust in technologies such as NHS app uptake, and for future research in global deployment strategies. This means a wide-ranging and continual assessment of legal frameworks and outcomes between companies, organisations and institutions for long-term data privacy planning. Strategies also include ensuring groups and individuals have faith in their data integrity in the cloud.

As necessary components of GDPR, the collecting, processing and deleting data remain a challenge. The enable user to fully engage with confidence, education and engagement with minorities, and with mental illnesses is an effective way to provide group assurances. As with different countries, data protection concepts and public engagement practices vary significantly. For anticipating any future disaster or pandemic scenario, it is clear that accountability through public engagement should help restore national and international trust. Also research needs to be undertaken to design and promote a flexible and global strategy to encompass technical solutions, operational resource strategy, and policy development. This would enhance data protection objectives, build population trust in government monitored apps and ultimately provide a successful and robust global protection strategy.

Blockchain for Security

Blockchain—integrity of data.

BC is one of the most commonly discussed DLT for ensuring the integrity of data storage and exchange in trust-less and distributed environments. It is a P2P decentralized distributed ledger [ 31 ] that can enable trusted data exchanges among untrusted participants in a network. BC systems such as Ethereum and Hyperledger fabric, have become popular BC frameworks for many BC-based software applications. Core features of BC such as immutability and decentralization are recognized by many sectors such as healthcare and finance to improve their operations. Although BC is a relatively new technology—just over a decade old—it seems to be revolutionary and there is a substantial number of research articles and white papers to justify this remark.

Blockchain—Cybersecurity

It is important to answer how emerging technologies such as BC can offer solutions to mitigate emerging cybersecurity threats and there is great research interest to study how BC can provide foundations for robust internet security infrastructures [ 32 ]. Many of the articles propose frameworks, prototypes and experimental beta BC-based solutions to problems in complex computing systems. Most of these experimental solutions are developed on Ethereum and Hyperledger fabric. In the case of Hyperledger fabric for example, this is due to its ease of software development, extensive customisability and interactivity.

Although Bitcoin is a most popular BC network, it has many cons such as its latency and great resource requirement. Some of practical solutions among them use innovative techniques to resolve critical cybersecurity issues. However, they imply infeasible changes to the existing system infrastructures that are difficult to readily test for efficiency and effectiveness when compared with conventional cybersecurity frameworks [ 33 ].

Blockchain—IoT

In our increasingly interconnected IoT world, there is a great need to improve cybersecurity. As explained in [ 34 , 35 ], cyber-attacks that exploit vulnerabilities in IoT devices raise serious concern and demand for appropriate mitigation strategies to tackle these threats. Ensuring integrity of data management and malware detection/prevention is an exciting topic of research [ 36 ].

It should be noted here that BC cannot eliminate cyber risks, but it can significantly minimize cyber threats with its core features. While most IT systems are built with cybersecurity frameworks that use advanced cryptographic techniques, they rely on centralized third-party intermediaries such as certificate authorities to ensure the integrity of their data management. Malicious parties can exploit weaknesses in such relationships to disrupt/penetrate these systems with cyber threats such as DDoS attack, malware, ransomware, etc.

Blockchain—Protocols

BC can resolve these issues due to its decentralization; it eliminates single points of failures and the need for third-party intermediaries in IT systems and ensures the integrity of data storage and exchange with encryption and hash functions [ 37 ] so that data owners can completely audit their data in the systems.

A BC network with many mutually trustless nodes is more secure than a network with few nodes that rely on trusted/semi-trusted centralized third-party intermediaries because, in a BC network, every node has a complete copy of the unique record of all transactions in the network that is maintained with the network consensus protocol. The robustness of a BC network i.e. its safety and security, depends on its decentralization, and this depends on its governance and consensus protocols. A good comparative study of DLT consensus protocols is provided by Shahaab et al. [ 38 ].

Blockchain—Summary

What are some future research directions and challenges for BC and Cybersecurity?

Consensus Protocols: Generally, public BC networks have high latency due to their consensus protocols. This makes them a non-starter for applications in real-time environment. Research on consensus protocols should be holistic and consider both, hardware and software, for such environments [ 39 ].

Cryptocurrencies: more research on cryptoassets is needed to tackle challenges to legal enforcement and forensics - both domestic and international—that enable cybercriminal activity such as terrorism financing.

IoT: As explained in [ 40 ], consortium BC networks can be used to improve the overall internet connectivity and access. Future research on IoT-BC integration should demonstrate feasible implementations that can be evaluated and compared with existing IoT solutions. They should also quantitatively study fault tolerance, latency, efficiency, etc. of BC-based IoT networks.

Data Analytics: BC can ensure the integrity of data and with AI/BD analytics it can be used to reduce risks and fraudulent activities in B2B networks. Hyperledger fabric is a DLT project that can be used for this relatively unexplored research areas.

Cybersecurity, Data Privacy and Blockchain

As stated in [ 41 ], BC-based digital services offer transparency, accountability and trust, however not one size fits all, as there are paradoxes between cybersecurity, GDPR compliance and the operation of BC. Haque et al., demonstrate in a systematic literature review regarding GDPR-BC compliance and highlights six major categories that are:

Data modification and deletion (Articles 16–18)

Default protection by design (Article 25)

Controllers/processors responsibilities (Articles 24, 26 and 28)

Consent management (Article 7)

Lawfulness and principles (Articles 5, 6 and 12)

Territorial scope (Article 3)

Haque et al. [ 41 ] states that use-cases of BC should be retrospectively applied in a way that can be made compliant to GDPR. The literature review also highlighted additional GDPR-BC research domains that include areas such as smart cities, information governance, healthcare, financial data and personal identity.

GDPR vs Blockchain

Vast amounts of PII are being collected, screened, and utilsed illegally due to cyber-espionage, phishing, spamming identity theft, and malpractice. BC on the other hand, due to the immutability in design and utility in tracking, storing and distributing DLT data, can clash with GDPR, especially with the “Right to be forgotten: Article 17”, including various rights to erasure [ 42 ]. Al-Zaben et al., proposes a framework that is on a separate off-chain mechanism that stores PII and non-PII in a different location. It is best to design and regulate network participation in fulfilling GDPR requirements, although not a perfect fit, this example shows how by design, a compliant use-case can be augmented in fulfilling parts of GDPR.

Ransomware Defense vs Blockchain

In [ 43 ], their paper describes that for malicious software to use configuration commands or information, malware has to be able to connect to the original owner. Therefore, a fairly new principle of domain generation is proposed, in that actively deployed ransomware is utilised to track user coordinates based on transactional data in a bitcoin BC. The gives a malware author the ability to dynamically change and update locations of servers in realtime.

Supply Chain Attack vs Blockchain

Recent and alarming increases in supply chain cyber attacks, has given various implementation strategies of BC in security of IoT data, that generally produces positive outcomes due to the transparency and traceability elements inherent in the technology by design. This paper highlights and discusses challenges to include many BC based systems in various industries, and focuses on the pharmaceutical supply chain. In conclusion, [ 44 ] states that the application of BCT can enhance supply chain security via authenticity and confidentiality principles.

Data Storage vs Blockchain

Due to the full-replication data storage mechanism in existing BC technologies, this produces scalability problems due to copying at each node, thus increases overall storage per-block [ 45 ]. Additionally, this mechanism can limit throughput in a permissioned BC. A novel storage system is proposed to enhance scalability by integrating erasure coding that can reduce data acquisition per block and enlarge overall storage capacity.

Of the many challenges that face legal, operational and performance criteria with utilising BC, it is clear to see that as we gather more and more personal data, endure more cyber attacks, and encounter storage disadvantages, many proposed frameworks seek to provide solutions that are only a part of compounding and escalating situation. The transactional speed and scalability of technologies such as BC, can hinder data protection rights, focused cyber-attacks, and the ability to update and track users, however there are advantages in creating separate mechanisms that when produced as a whole, that can indeed support data verification, transparency and accountability in many industries.

Results: Brief Overview of Intelligent Framework

Key Data Management Architecture Components: Fig.  1 shows the block diagram of the proposed framework. Key components of the framework are explained and synthesised in the following paragraphs.

Data flow audit mechanism

Blockchain: Data Storage and Immutability

To provide system accountability, transparency and traceability from network system traffic point of view, an article by Kumar et al., 2020 demonstrates how DLT systems are applied in e-commerce to include health medicines, security devices, food products to ensure BC technological and e-commerce sustainability. Also, [ 46 ] presents a study that explores the potential of DLT in the publication industry and present a technological review. The studies demonstrate how research is being explored and influencing DLTs globally alongside their synergies of application across academic, private and public sectors.

Standardisation of IoT Interface Portal

For purposes of legal acquisition and processing of data with consent, users can connect from IoT smart devices and appliances, such as; smart phones, sensors, tablets and user desktops. User applications and interfaces also provide a level of protection by design in most cases, however the applications can also compound and conflict with each other to produce security vulnerabilities (e.g. Cookies). Networks include; Cellular, Local and Personal Area Networks (PAN/LAN), Low Power Wide Area Networks (LPWAN) and Campus Area Network (CAN) carrier methods operate and maintain IoT system stability. Some IoT devices are capable of ensuring seamless connectivity in data access. However, at the point of access, a user interfaces with a given IoT device could be one of multiple architectures that present challenges in correctly identifying and processing data in a legal, reliable and consistent fashion. Therefore an overarching framework to ensure a standardised system whilst mitigating risk (security Vulnerabilities) is catered for in utilising network protocols with a prescribed profile limited to key information such as, Personal Identification Number (PIN), Account Number and password encryption.

Administrator 1: Public LAN/WLAN/CAN

A main purpose here is the execution of network communication protocols for the processing and or keeping (storage) of PII and data access control to include cryptography. At the level of an SME, the types of regulatory compliance’s necessary to operate as a business include a retrospective and current auditable trail to demonstrate good practices. A selection of operational scenarios are to be emulated (e.g. from case law) in the preparation of codifying, selecting and the setting of chosen principles, standards and legal frameworks. Other objectives to explore include, Confidentiality, Integrity, Availability and Data Minimisation. As shown in [ 47 ], stakeholders are required to initialise and validate a product block, this activates the wallet, to include pseudo-identity generation with a public and private key pair. The keys are utilised for signature and verification processes. Here, administrator 1 oversees and combines the execution of network communication policies to govern a user or a given set of protocols.

Administrator 2: Private LAN Network

The function of the administrator here is to utilise criteria to facilitate accountability, transparency and traceability from network system traffic. Data entry points provide group integrity as each user, or entry, is available for all to see. More fundamentally, this data will help inform, develop, calibrate and test the setting of audit and assessment parameters. The information is then combined, contrasted and compared to the Administrator 1 data collection. Resulting information then updates the Valid Data Acquisition IDPS System and Cyber-Detection Methods (e.g. Packet Sniffing) of Network Packet Data communication protocols with data effective access control. In this case, Administrator 2 provides an array of users insights into the performance of ISO 27001 and DPA/GDPR policies to identify optimum operational cost in various prescribed operating scenarios. Through analysis with tools such as BD Analytics and ML for example, nuanced data, pattern identification and aggregation provides a basis for speculation as to an ideal operating system from within a business.

Smart Contract: Agreement or Terms of Contract

Unfortunately, maintaining these systems incur at significant cost, on the other hand, these systems also cut out the “middle-man” and save resources to empower individuals and business owners. For example, individual and group scenarios are negotiated and interpreted between users in partnerships. In emulating this function, key objectives are identified and embedded from legal frameworks to produce an automatic transaction protocol with consensus in the implementation of a codex (e.g. OPCODES). Therefore, a codex of legal precedent and statutory instrumental data protection, data operation and dissemination laws will be emulated to start. The codex is the library and framework that enables partners to equitably participate in a sustainable and trust-less operational environment. In utilising ISO 27001 for example, a collection of policies are negotiated and agreed upon prior to formally undertaking a contract between parties. Therefore, GDPR and ISO 27001 are transcribed, layered and mapped with verification mechanisms derived from case-law and by design into a SC agreement. This dynamic process forms the centre of any given exchange or process of data acquisition and data dissemination.

To enable an effective cybersecurity strategy for SME’s and alike, government and private sector finance initiatives are key. This includes awareness and training for management, with oversight and additional support for staff to incorporate ML and AI into the workplace more effectively. Intrusion, detection and prevention policy from SME to government level can then flourish in promoting and sustaining the full benefits and protections of cybersecurity from cyber-criminality. However, for global data security coverage, the concept in itself is interpreted differently as the legal, ethical and consensual implementation challenges remain formidable as a result. Acquiring personal data from regional divisions to aide authorities in resource strategy at this scale, requires trust in institutions and technologies to be fully beneficial to all.

Accountability and transparency efforts also require the continual assessment of legal frameworks, systems and outcomes, with generous investment from public and private sectors. Public awareness, perception and confidence levels in the justice system through transparency and education, with focus to include mental illness and minority group engagement policies, can benefit societies substantially. The earlier proposed framework from research, demonstrates a robust and complex strategy, however looking to the future, BC network latency present real-time challenges to assist SME technology adoption. Increasing digitalisation and decentralisation leads to diverse communications, hence creating a wider array of participants to collaborate and share. However, these digital systems are not mature in terms of security and inevitably create attack space for attackers.

In this review paper, we highlighted several security problems that arise in digital systems, computation data and associated trust mechanisms. These challenges have resulted in evolution of technical solutions. Current solutions are so diverse that range from preliminary in small organisations to the state-of-the-art in mega-organisations. The cyber landscape is likely to change even further that necessitates robust solutions. This paper also brings in research from different collaborators with the potential to identify the challenges and move in the direction of designing novel solutions. This we believe as a result, will enhance and lead to secure cyber systems which achieve data security comprehensiveness.

Rawindaran N, Jayal A, Prakash E. Artificial intelligence and machine learning within the context of cyber security used in the UK SME Sector. In: AMI 2021— the 5th advances in management and innovation conference 2021. Cardiff Metropolitan University. 2021.

Wylde V, Prakash E, Hewage C, Jon. Platts. Covid-19 Crisis: Is our Personal Data Likely to be Breached? In AMI 2021 - The 5th Advances in Management and Innovation Conference 2021. Cardiff Metropolitan University, 2021.

Balasubramanian R, Prakash E, Khan I, Platts J. Blockchain technology for healthcare. In: AMI 2021—the 5th advances in management and innovation conference 2021. Cardiff Metropolitan University; 2021.

Gallaher MP, Link AN, Rowe B. Cyber security: economic strategies and public policy alternatives. Chentanham: Edward Elgar Publishing; 2008.

Google Scholar  

Zarpelão BB, Miani RS, Kawakani CT, de Alvarenga SC. A survey of intrusion detection in Internet of Things. J Netw Comp Appl. 2017;84:25–37.

Article   Google Scholar  

Are Your Operational Decisions Data-Driven? 2021. https://www.potentiaco.com/what-is-machine-learning-definition-typesapplications-and-examples/ . Accessed 11 Jul 2021.

Biju SM, Mathew A. Internet of Things (IoT): securing the next frontier in connectivity. ISSN. 2020.

Cahn A, Alfeld S, Barford P, Muthukrishnan S. An empirical study of web cookies. In: Proceedings of the 25th international conference on world wide web; 2016. pp. 891–901.

Cressy R, Olofsson C. European SME Financing: An Overview. Small Business Economics, 1997. pp 87–96.

General Data Protection Regulations (GDPR). https://ico.org.uk/for-organisations/guide-to-dataprotection/guide-to-the-general-data-protectionregulation-gdpr/ . Accessed 16-10-2020.

Roesch M, et al. SNORT: lightweight intrusion detection for networks. Lisa. 1999;99:229–38.

Dunham K, Melnick J. Malicious bots: an inside look into the cyber-criminal underground of the internet. Boca Raton: Auerbach Publications; 2008.

Book   Google Scholar  

Kabiri P, Ghorbani AA. Research on intrusion detection and response: a survey. Int J Netw Secur. 2005;1(2):84–102.

Fraley JB, Cannady J. The promise of machine learning in cybersecurity. In: SoutheastCon 2017, IEEE; 2017. pp. 1–6.

Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor. 2015;18(2):1153–76.

Machine learning algorithm cheat sheet for azure machine learning designer. 2021. https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet . Accessed 3- Mar 2021.

Anthi E, Williams L, Rhode M, Burnap P, Wedgbury A. Adversarial attacks on machine learning cybersecurity defences in industrial control systems. J Inf Secur Appl. 2021;58:102717.

Catak E, Catak FO, Moldsvor A. Adversarial machine learning security problems for 6G: mmWave beam prediction use-case. arXiv:2103.07268 .2021.

Guinchard A. Our digital footprint under Covid-19: should we fear the UK digital contact tracing app? Int Rev Law Comput Technol. 2021;35(1):84–97.

Tran J, Ngoc C. GDPR handbook for record of processing activities. Case: the color club A/S. 2020.

Raman R, Achuthan K, Vinuesa R, Nedungadi P. COVIDTAS COVID-19 tracing app scale-an evaluation framework. Sustainability. 2021;13(5):2912.

Juneidi JS. Covid-19 tracing contacts apps: technical and privacy issues. Int J Adv Soft Comput Appl. 2020;12:3.

Majeed A. Towards privacy paradigm shift due to the pandemic: a brief perspective. Inventions. 2021;6(2):24.

Black M, Lee A, Ford J. Vaccination against COVID-19 and inequalities-avoiding making a bad situation worse. Public health in practice. England: Elsevier; 2021.

Zaeem RN, Barber SK. The effect of the GDPR on privacy policies: recent progress and future promise. ACM Trans Mgmt Inf Syst. 2020;12(1):1–20.

Antal CD, Cioara T, Antal M, Anghel I. Blockchain platform for COVID-19 vaccine supply management. 2021. arXiv:2101.00983 .

How Blockchain is helping in the fight against Covid-19. 2021. https://www.lexology.com/library/detail.aspx?g=8b5ef0f0-05b3-4909-b5d5-da7bd57f0381 . Accessed 24 Apr 2021.

Razai MS, Osama T, McKechnie D, Majeed A. Covid-19 vaccine hesitancy among ethnic minority groups. 2021.

Robertson E, Reeve KS, Niedzwiedz CL, Moore J, Blake M, Green M, Katikireddi SV, Benzeval MJ. Predictors of COVID-19 vaccine hesitancy in the UK Household Longitudinal Study. Brain Behavior Immunity. 2021.

MacKenna B, Curtis HJ, Morton CE, Inglesby P, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Bates C, et al. Trends, regional variation, and clinical characteristics of COVID-19 vaccine recipients: a retrospective cohort study in 23.4 million patients using OpenSAFELY. 2021.

Zheng Z, Xie S, Dai H, Chen X, Wang H. An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE international congress on big data (BigData Congress); 2017. pp. 557–64.

Salman T, Zolanvari M, Erbad A, Jain R, Samaka M. Security services using blockchains: a state of the art survey. IEEE Commun Surv Tutor. 2019;21(1):858–80.

Zhang R, Xue R, Liu L. Security and privacy on blockchain. ACM Comput Surv. 2019;52:3.

Pinno OJA, Gregio ARA, De Bona LCE. ControlChain: blockchain as a central enabler for access control authorizations in the IoT. In: GLOBECOM 2017—2017 IEEE global communications conference; 2017. pp. 1–6.

Mandrita B, Junghee L, Choo KKR. A blockchain future for internet of things security: a position paper. Dig Commun Netw. 2018;4(3):149–60.

Kshetri N. Blockchain’s roles in strengthening cybersecurity and protecting privacy. Celebrating 40 years of telecommunications policy—a retrospective and prospective view. Telecommun Policy. 2017;41(10):1027–38.

Ali M, Nelson J, Shea R, Freedman Freedman MJ. Blockstack: a global naming and storage system secured by blockchains. In: 2016 USENIX annual technical conference (USENIX ATC 16), pp 181–194. Denver, CO, 2016. USENIX:Association. 2016.

Shahaab A, Lidgey B, Hewage C, Khan I. Applicability and appropriateness of distributed ledgers consensus protocols in public and private sectors: a systematic review. IEEE Access. 2019;7:43622–36.

Taylor PJ, Dargahi T, Dehghantanha A, Prizi RM, Choo KKR. A systematic literature review of blockchain cybersecurity. Dig Commun Netw. 2020;6(2):147–56.

Alphand O, Amoretti M, Claeys T, Dall’Asta S, Duda A, Ferrari G, Rousseau F, Tourancheau B, Veltri L, Zanichelli F. IoT Chain: a blockchain security architecture for the internet of things. In: 2018 IEEE wireless communications and networking conference (WCNC); 2018. pp. 1–6.

Haque AB, Najmul Islam S, Hyrynsalmi AKM, Naqvi B, Smolander K. GDPR compliant blockchains-a systematic literature review. IEEE Access. 2021;9:50593–606.

Al-Zaben N, Hassan O, Mehedi M, Yang J, Lee NY, Kim CS. General data protection regulation complied blockchain architecture for personally identifiable information management. In: 2018 international conference on computing, electronics communications engineering (iCCECE); 2018. pp. 77–82.

Pletinckx S, Trap C, Doerr C. Malware coordination using the blockchain: an analysis of the cerber ransomware. In: 2018 IEEE conference on communications and network security (CNS); 2018. pp. 1–9.

Johny S, Priyadharsini C. Investigations on the implementation of blockchain technology in supplychain network. In: 2021 7th international conference on advanced computing and communication systems (ICACCS); 2021. pp. 1–6.

Qi X, Zhang Z, Jin C, Zhou A. A reliable storage partition for permissioned blockchain. IEEE Trans Knowl Data Eng. 2021;33(1):14–27.

Paruln K, Gulshan K, Geetha G. Exploring the potential of distributed ledger technology in publication industry—a technological review. In: CEUR Workshop Proceedings. 2021.

Kumar G, Saha R, Buchanan WJ, Geetha G, Thomas R, Rai MK, Kim T, Alazab M. Decentralized accessibility of e-commerce products through blockchain technology. Sustain Cities Soc. 2020;62:102361.

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Wylde, V., Rawindaran, N., Lawrence, J. et al. Cybersecurity, Data Privacy and Blockchain: A Review. SN COMPUT. SCI. 3 , 127 (2022). https://doi.org/10.1007/s42979-022-01020-4

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• Published: 11 January 2024

Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques

• K. Venkatesan 1 , 2 &
• Syarifah Bahiyah Rahayu 1   nAff2  

Scientific Reports volume  14 , Article number:  1149 ( 2024 ) Cite this article

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In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

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Introduction.

The Consensus protocols employed in the blockchain network provide high security and more efficient operations. Hybrid consensus algorithms are developed by combining the key elements of various consensus algorithms. This might be useful to prevent double-spending and 51% of attacks. Combining Proof of Work (PoW) and Delegated Proof of Stake (DPoS) improves computation performance and enhances high security 1 . DPoS is used for block validation, and PoW is used for block creation, making it more difficult for an attacker to control the network. The combination of Proof of Stake (PoS) and Proof of Work (PoW) results in better security performance along with the network's decentralization 2 . PoS is used for block validation, and PoW is used for block creation, increasing the network's security and decentralization 3 . Integrating the DPoS and Practical Byzantine Fault Tolerance (PBFT) provides higher security, scalability, and efficiency. Here, DPoS is used for block creation, and PBFT is used for block validation, providing better security and scalability 4 .

Hybridization of the Casper and PBFT consensus algorithms can provide a higher level of security against 51% of attacks in blockchain technology. Casper Algorithm adopts PoS for block validation 5 . Here, validators are selected based on their network stake size. PBFT works differently compared to other algorithms. It confides a group of validators to grasp the following block consensus. Hybridization of PoS and PBFT leverages high-level security and rapid consensus time. This makes it very difficult for attackers to access the network and perform cyber-attacks. In addition, consensus hybridization balances scalability and decentralization, which is convenient for microgrid networks 6 . For any algorithm, achieving a complete security audit and system update, which confirms the efficacy, is essential. Compared to other individual consensus algorithms, these hybridization algorithms enhance security by preventing 51% of attacks 7 . However, analyzing the trade-offs before implementing them in blockchain networks is mandatory. Figure  1 shows the basic block diagram using the blockchain network and ML techniques.

Basic block diagram using blockchain and ML techniques.

Many researchers propose a new consensus protocol that performs better security and scalability and reduces the probability of cyber-attacks on the blockchain network. In 8 , the author proposed Algorand, which achieves transaction finality and high scalability 9 introduces consensus algorithms, which enhance throughput, scalability, and security. The authors Zhu 10 introduced the GHOST protocol, the modified PoW consensus protocol, which achieves the best security and high throughput transaction. Meanwhile 11 , A. Kiayias et al. propose ouroboros, the modified PoS protocol that enhances scalability and security. Lashkari et al. 12 eliminate 51% of attacks using Bitcoin-NG, improving throughput and scalability. These works accentuate the necessity of creating new consensus protocols that perform highly scalable security and have the potential to handle vast volumes of transactions.

The Hybrid consensus algorithms with Machine Learning (ML) techniques gained significant approaches, which led to high performance, improved scalability, and enhanced security in blockchain networks. Several researchers, such as Zhang 13 , Andoni 14 , Wang 15 , and Yang 16 , conduct various simulations and experiments and discusses hybrid protocols, which evaluate their efficacy and perform better results and security than the existing consensus mechanisms. Additionally, Mahmood et al. 17 propose a comprehensive review of consensus protocols that enlighten the potential of ML approaches for enhancing performance and security. The author highlights the hybrid consensus mechanism and ML integration to achieve optimal block validation and improve network performance.

Over the years, the performance of the hybrid consensus protocol combined with Particle Swarm Optimization (PSO) has been studied. In 18 , Zhu et al. propose a PoS-based consensus model with PSO that achieves high scalability, better performance, and enhanced security. Razali et al. 19 Introduce a new consensus model, which uses PSO for block validation optimization. Ali et al. 20 Work on hybrid consensus protocol with PSO approaches, which results in high security and scalability and maintains high performance. Ullah et al. 21 propose ML-based PSO for faster block validation and better throughput. Kumar et al. 22 Provide a comparative study of existing consensus protocol and highlight the potential of the hybrid consensus protocol with PSO optimization techniques. It also demonstrates the strength of hybridization and achieves better performance and scalability, enlightening future research requirements in blockchain networks.

Unsupervised, supervised, and rule-based ML approaches 23 are vital in responding to and detecting microgrid attacks in blockchain networks. ML techniques may also be used to solve communication and behavior-based attacks. ML-based hybrid consensus algorithms improve security and other performance factors and provide a scope for active research 24 . Specifically, new consensus protocols with ML approaches detect and prevent the significant threats of attacks in blockchain networks. These papers propose simulation models and demonstrate experiments that can perform better security than any other consensus mechanisms 25 . Additionally, these papers provide a comprehensive overview, realize the potential of hybridization, and highlight the importance of further research to develop more effective and efficient methods to secure blockchain mechanisms 26 .

However, the researchers propose a diverse solution, develop a creative approach, and offer a state-of-the-art framework to overcome cybersecurity issues. In 27 , the author proposes Fed-Inforce-Fusion, which is a reinforcement learning-based fusion model for the Internet of Medical Things (IoMT) networks. This method incorporates federated and reinforcement learning to improve accuracy and detection. Meanwhile, the "PC-IDS" framework uses hybrid machine learning approaches to identify harmful behaviors and secure privacy in cyber-physical power networks 28 . However, deep autoencoder IDS performs well and accurately in real-time intrusion detection in IIoT networks 29 .

Additionally, the multi-stage AV framework combines the state-of-the-art framework and deep learning techniques, outperforming existing systems in understanding and identifying cyber risks within autonomous vehicles 30 . Therefore, the relevance of these studies lies in their exploration of hybrid consensus algorithms and integration of machine learning approaches, offering valuable insights into blockchain and cyber security attacks. Despite their contributions, several limitations were prevalent in the studies reviewed. These limitations underscore the need for further research to address these gaps and refine our understanding of integrating machine-learning approaches in hybridizing consensus algorithms.

Open challenges and motivation

Consensus algorithms face several open challenges that need to be addressed for the widespread adoption of blockchain technology in real-world applications. One of the primary challenges is scalability , as consensus mechanisms must efficiently handle many transactions per second without compromising security and decentralization. Another critical challenge is energy efficiency , particularly in Proof of Work (PoW), where the high energy consumption is unsustainable and costly in the long run. Developing energy-efficient consensus algorithms or improving existing ones is vital for practical applications. Ensuring fast transaction confirmation times is another challenge to meeting the demands of real-time applications. Long confirmation times can hinder the usability of blockchain technology, making it necessary to optimize latency and transaction confirmation. Security is an ongoing concern, and consensus mechanisms must resist attacks such as double-spending, Sybil, and 51% attacks. Enhancing security measures is crucial for building trust and widespread adoption.

Governance and compliance are significant challenges, as consensus algorithms must align with legal and regulatory requirements while maintaining decentralization. Finding the right balance between compliance and decentralization is crucial for the finance, healthcare, and supply chain management industries. Interoperability between blockchain networks and consensus algorithms is essential for collaboration and communication across systems. Developing standards and protocols for seamless integration and data exchange is challenging. Privacy and confidentiality are paramount, and consensus algorithms must incorporate robust techniques to protect sensitive data while maintaining transparency and audibility. Consensus mechanisms should adapt to dynamic networks , where nodes can join or leave anytime and handle challenges such as node churn, network partitions, and malicious nodes. Improving the user experience is vital for widespread adoption, requiring consensus algorithms to minimize transaction fees, reduce latency, and provide a seamless and intuitive interface.

Addressing the environmental impact of consensus algorithms, particularly energy-intensive ones like PoW, is a pressing challenge. Developing sustainable and eco-friendly consensus algorithms is essential to align blockchain technology with global sustainability goals. Overcoming these challenges will require continuous research, innovation, and collaboration between industry, academia, and regulatory bodies. By addressing these open challenges, consensus algorithms can pave the way for the widespread adoption of blockchain technology in various real-world applications.

Research gap identification

The analysis of existing literature reveals a distinct gap in integrating hybrid consensus algorithms with machine learning approaches. While previous studies have laid essential groundwork, there remains unexplored territory in understanding the concepts of hybridization and difficulties in integrating machine learning approaches to enhance the security of the blockchain network. This research strives to fill this gap by defining the research objectives, thereby advancing our understanding of improving security in blockchain networks and protecting the network from malicious attacks.

Research objectives

The main goal of this research paper is to implement a hybrid consensus mechanism with ML techniques, which enhances the security of the consensus mechanisms and avoids cyber attacks. The contribution of this research is listed below.

To identify and understand the vulnerabilities in existing mechanisms. Here, analyzing and evaluating the security shortcomings enables a comprehensive understanding of the vectors and potential threats.

To reframe the hybrid consensus Algorithms. A comprehensive analysis of consensus algorithm hybridization and its necessity in cyber security is discussed here.

To develop the ML framework for extraction of the features and anomaly detection. The critical aspects of the ML framework that effectively performs feature extraction, anomalies, and malicious activity detection within the blockchain network must be performed. This framework will leverage advanced ML algorithms to analyze network behavior, identify suspicious patterns, and distinguish normal activities from potential attacks.

To integrate the ML framework with consensus mechanisms. The developed ML framework will be integrated into the consensus algorithms discussed in this work to enhance the security of consensus mechanisms. This integration will enable real-time monitoring and proactive defense mechanisms against attacks, thereby ensuring the integrity and stability of blockchain networks.

To evaluate the effectiveness of the hybrid approach. The proposed hybrid ML approach can be thoroughly evaluated. The evaluation would be focused on security enhancements achieved through the proposed solution, which leads the system to an adoptive selection of consensus algorithms and intelligent decision-making.

By addressing these challenges, this research paper aims to contribute to blockchain security by proposing novel consensus algorithms and hybrid ML approaches that enhance the overall security of blockchain networks and mitigate the risk of cyber-attacks.

The contribution of the proposed hybrid consensus algorithms is listed below.

This proposed hybridization combines the strength of different consensus mechanisms, mitigates the vulnerabilities, and enhances scalability.

The hybrid consensus algorithm uses energy-efficient mechanisms to reduce environmental impact without compromising security.

This model can adapt to dynamic conditions and confirms robustness.

This proposed method can integrate privacy-enhancing features and protect sensitive information.

Additionally, hybrid models can improve trust by providing robust and resilient consensus mechanisms.

The contributions of the ML techniques are listed below:

ML can greatly enhance threat detection capabilities and improve the trustworthiness of the network by providing a multilayer defense.

It improves the adaptability to emerging threats and acts as an intelligent layer to optimize consensus mechanisms based on network conditions and improve scalability challenges.

ML techniques can adjust the consensus mechanism based on energy availability and consumption patterns.

It also learns continuously from the network behavior and adjusts security measures to meet network dynamics.

Cryptographic techniques and advanced privacy-preserving algorithms can further enhance the confidentiality of transactions and user data.

Decentralized systems can detect and respond to security threats using machine learning to identify patterns and anomalies, reducing the risk of successful attacks.

Resource allocation can be optimized by dynamically assessing threat levels and adjusting security measures accordingly. This ensures efficient allocation of resources to areas with the highest risk.

The rest of the paper is organized as follows: Section " Background study " establishes the preliminaries for the challenges and vulnerabilities in existing consensus mechanisms. A detailed discussion of the previous research on 51% of attacks and their impact is also presented in this section. ML Techniques for Security Enhancement are discussed in Section " Machine learning techniques for security enhancement ". Section " Research methodology and materials " presents the research methodology and materials and its advantages and optimizations achieved through the proposed approach. The experimental implementations and results are discussed in Section " Security enhancements achieved through the proposed solution ". Section " Open issues and challenges of the hybrid consensus approach " presents the proposed hybrid consensus approaches' open challenges and future scope. The conclusions and future work of the paper are discussed in Section " Conclusion ".

Background study

Challenges and vulnerabilities in existing consensus mechanisms.

The progress in blockchain technology has played a crucial role in addressing challenges related to decentralization, security, and consensus formation using various consensus mechanisms. However, these mechanisms have their own set of challenges and vulnerabilities. This section will delve into the concerns and susceptibilities of existing consensus mechanisms.

Proof of work (PoW)

PoW is a consensus algorithm that exhibits several challenges and vulnerabilities. Firstly, PoW requires substantial computational power and energy consumption to solve complex cryptographic problems, resulting in high energy consumption that is environmentally unsustainable 31 . This energy-intensive nature raises concerns about the ecological impact of blockchain networks utilizing PoW. Secondly, scalability becomes a concern as the network grows, as the PoW algorithm needs help to maintain efficient consensus and transaction validation in the face of increasing participant numbers 32 . The computational requirements become increasingly demanding, potentially limiting the scalability potential of PoW-based blockchains.

Additionally, the concentration of mining power in a few large mining pools introduces concerns regarding mining centralization and the potential for collusion 33 . This centralization raises questions about the democratic nature of the blockchain network and the potential for malicious activities by a concentrated mining power 34 . The priority toward long-term sustainability and PoW-based blockchain adoptions are required to address the challenges.

Proof of stake (PoS)

The Proof-of-Stake (PoS) consensus protocols face vulnerabilities and challenges that include the possibility of wealth concentration and must be addressed within the network 35 . In PoS, validators are chosen based on the number of coins they have staked, meaning that those with more enormous stakes are more likely to be selected as validators. This wealth concentration can undermine the network's decentralization and compromise its security 6 . Another issue is the nothing at stake problem, where validators can vote on multiple forks simultaneously without consequences. Validators can create forks and change transaction history, which may lead to double-spending attacks 36 . To mitigate these challenges, solutions such as implementing penalties for malicious behavior, implementing robust governance mechanisms, and ensuring widespread participation can help maintain the security and integrity of the PoS network. Additionally, ongoing research and development efforts are needed to address these vulnerabilities and improve the overall robustness of the PoS consensus mechanism 37 . PoS has energy efficiency advantages, but its vulnerabilities must be acknowledged to ensure blockchain network security.

Delegated proof of stake (DPoS)

The DPoS algorithm is widely adopted for its effectiveness and scalability; however, it poses significant challenges and vulnerabilities. A primary concern is the potential for centralization, as DPoS relies on a small group of trusted delegates responsible for block production and validation 38 . This concentration of power introduces the risk of influential entities engaging in vote buying or collusion, compromising decentralization and fairness. Another area for improvement is low voter participation, leading to a lack of representation and potential governance problems 39 . In order to overcome these challenges, measures should be implemented to promote decentralization and encourage greater voter engagement. This includes preventing manipulation through mechanisms that safeguard the integrity of the election process 40 . Educating token holders about the importance of voting and its impact on network governance can increase participation. Introducing mechanisms for delegate rotation or limiting their terms can prevent long-term centralization 41 . Through continuous research and protocol improvements, DPoS can balance efficiency and decentralization, offering a more inclusive consensus mechanism for blockchain networks by enhancing transparency and promoting active participation 42 .

Practical byzantine fault tolerance (PBFT)

PBFT offers the advantage of withstanding Byzantine faults but also presents challenges and assumptions that must be addressed. Scalability is a limitation of PBFT, as the latency increases with more nodes due to the communication required for consensus 43 . This makes PBFT more suitable for smaller networks or consortium blockchains. The assumption of PBFT regarding the number of faulty nodes is critical, as it assumes that at most one-third of nodes are faulty. When a higher proportion of nodes become malicious or inaccurate, PBFT's ability to maintain consensus can be compromised 44 . However, researchers have dedicated their efforts to strengthening the scalability and resilience of PBFT in order to overcome these challenges 45 . Optimizations like parallelization and batching have been proposed to reduce communication overhead and latency in more extensive networks. Fault-tolerant algorithms and Byzantine fault detection techniques aim to handle situations where the assumed threshold of faulty nodes is exceeded 46 . Hybrid consensus models combining PBFT with Proof of Stake or Proof of Work have also been explored to balance scalability and fault tolerance. Researchers have proposed various methods to overcome these challenges and improve the scalability, durability, and usability of PBFT in various blockchain networks 47 .

Proof of authority (PoA)

PoA relies on a predetermined set of approved validators responsible for validating and adding new blocks to the blockchain. While PoA offers certain benefits, it also poses notable limitations and vulnerabilities. A key concern is the potential for centralization 48 . However, carefully selecting validators and proactive measures to prevent infiltration can ensure continued decentralization and strengthen network security, leading to a robust and resilient system. The concentration of authority in the hands of a small group of validators contradicts the decentralization goal that blockchain technology aims to achieve 49 . Another challenge in PoA is the need for incentives for validators. Unlike Proof of Work and Proof of Stake, PoA does not provide incentives or rewards to validators, as block creation authority is based on reputation or identity rather than a commitment of resources. The absence of incentives will lead to lower participation and highly compromise the network's security 50 . Validators may become less vigilant or refrain from active participation in block validation, leading to a less secure and reliable network. Certain modifications have been proposed for PoA to address these challenges. For instance, implementing a reputation-based system or penalties for misbehavior can mitigate the risk of collusion among validators 51 .

Additionally, offering incentives in the form of transaction fees or token awards can promote active participation and ensure the stability and security of the network. The PoA system offers faster transactions and uses less energy. However, there are concerns about security and trust because it relies on a set of predetermined validators. Additionally, when implementing PoA, it is necessary to consider the balance between centralization and decentralization 50 .

Casper presents a more secure and reliable consensus mechanism, addressing challenges traditional proof-of-stake (PoS) algorithms face. However, a significant challenge in Casper lies in parameter selection. Careful consideration is required to balance security, liveness, and fault tolerance 52 . Improper parameter values can lead to vulnerabilities and compromise the protocol's effectiveness. Conservative parameters may hinder efficient block finalization, while permissive parameters can increase the risk of malicious behavior 53 . Thorough analysis and understanding of network dynamics and trade-offs are necessary to determine appropriate parameter values for Casper. Another critical aspect of Casper is using slashing conditions to penalize validators for malicious behavior 54 . However, defining and enforcing slashing conditions without introducing false positives or negatives is a complex challenge. False positives penalize honest validators wrongly, while false negatives allow malicious validators to escape penalties. Striking the right balance is crucial to prevent unfair penalization and ensure appropriate punishment 55 . Designing robust and accurate slashing conditions requires careful consideration and analysis to minimize false positives and negatives.

Researchers are enhancing Casper to address system challenges through better parameter selection and slashing conditions. This method involves rigorous empirical analysis, simulation studies, and formal verification techniques 56 . It also enables us to confidently determine the most influential parameter choices and optimize the settings for feasible results. Extensive testing and experimentation assess the impact and behavior of slashing conditions in real-world scenarios 57 . Future advancements may involve automated or adaptive parameter selection mechanisms that dynamically adjust based on network characteristics. Similarly, improvements in slashing conditions can be achieved through ML or incorporating external reputation systems. Continued research and development efforts will lead to more robust and practical implementations of Casper.

Previous research on cyber 51% of attacks and their impact

Previous research on 51% of attacks has explored the potential risks and consequences of these attacks in blockchain networks. By gaining control over 50% of the network's mining power, an attacker can manipulate the blockchain's transactions, potentially leading to double spending or other fraudulent activities . It is necessary to assimilate the importance of 51% of attacks for establishing adequate security and authenticity maintenance of the blockchain network.

Attack vector identification

This is a challenging research problem related to 51% of attacks. Several studies and experiments have been conducted to understand the various attack vectors. By analyzing these vectors, researchers can enhance security and improve countermeasures. Mining centralization is one of the prominent attacks. Studies are performed to examine the mining power concentration 58 . This study includes investigating the distribution of mining resources, examining the incentives for mining centralization, and identifying the potential risks associated with such centralization. Network partitioning usually occurs when we split the blockchain network into multiple subnets. These results from various technical issues, intentional attacks, and other network disruptions, which blockchain networks need to address 59 . However, researchers have analyzed the influence of network partitioning on consensus mechanisms and their vulnerabilities. After examining these problems, researchers aim to mitigate the issues connected with network partitioning and ensure blockchain integrity. Rent attacks acquire more computational power and control the hash rate's prominence, enabling attackers to manipulate the transaction. Researchers can examine the influence of this attack on other consensus mechanisms, which emphasize the importance of prevention protocols to avoid the presence 20 . However, researchers can also investigate the chance of group mining to launch the attack. When mining pools or entities collude with each other, they combine the computational power and control the hash rate, as discussed in the rental attack, and manipulate the transaction in the blockchain network 60 . This results in the importance of protection mechanisms to avoid and reduce collusion between the mining entities. Furthermore, researchers also analyzed these attacks to detect and mitigate the related vulnerabilities.

Double spending and transaction reversal

These attacks aim to achieve double-spending and transaction reversal, exploiting vulnerabilities in the transaction verification process. By controlling most of the network's mining power through a 51% attack, an attacker can manipulate the blockchain's transaction history, allowing them to spend the same coins multiple times 61 . Previous research has extensively explored the economic incentives and feasibility of double-spending attacks, considering factors such as attack cost, potential gains, and impact on the network's reputation 62 . Evaluating the economic viability of these attacks helps understand their motivations and enables countermeasures to be developed. Proposed countermeasures include increasing the number of confirmations required for transaction finality, implementing mechanisms for detecting suspicious transactions, and enhancing consensus algorithm security 63 . Advancements in blockchain technology have introduced additional measures to mitigate double-spending risks, such as faster block confirmation times and additional security layers like two-factor authentication and multi-signature transactions 64 . Research on the economic incentives and potential impact of double-spending attacks has led to the developing enhanced security protocols, promoting trust and reliability in cryptocurrency transactions.

Blockchain security and trust

Blockchain security and trust are critical considerations in designing and operating blockchain networks. The occurrence of 51% of attacks represents a significant threat to the security and trustworthiness of these networks. Extensive research has been conducted to assess the impact of such attacks on transaction integrity and overall reliability 65 . Successful attacks will compromise the accuracy and reliability of blockchain technology. Controlling network-mining power will lead attackers to manipulate the transaction history. This will cause double-spending attacks and confirms the earlier transactions 66 . This action will destroy the confidence of the user in the blockchain network. This will make users stumble on network transactions and cause fraudulent or fearful activities. However, the blockchain network always depends on user participation and adopting successful transactions, which causes economic repercussions 67 .

Earlier studies discussed the significant consequences of these attacks and recommended the importance of security measures to protect 68 . In addition, researchers are also focusing on mitigating and detecting 51% of attacks, which improves consensus mechanisms, decentralization governance, and enhancing network resilience 69 . Addressing the security and trust issues requires multi-faceted approaches. This approach involves technical solutions, regulatory measures, governance frameworks, and other industrial standards 70 . Research collaboration with policymakers, industrialists, and stakeholders ensures effective practices and security measures. Educating users and stakeholders will establish trust, comprehensive adoption, and usage.

Countermeasures and prevention

Hybrid consensus protocol combines PoW, PoS, and other mechanisms to improve security and avoid these attacks. These models will leverage the strengths of the approaches and mitigate the weaknesses. For instance, Hybrid PoW and PoS will improve security and reduce the mining centralization problem, which addresses the nothing at stake in PoS 71 . Likewise, hybrid PBFT leads to an increase in fault tolerance and scalability. These models can improve security and avoid 51% of attacks 72 .

Improving mining decentralization is also another preventive measure that has been proposed by researchers 73 . Promoting a distributed network of miners may lower the mining concentration power, which makes it more difficult for single or group entities to control the computational resources 74 . This technique can mitigate the problem of 51% of attacks through power distribution among various participants and confirms diverse and resilient networks. Introducing penalties for adverse behavior is vital to discouraging and avoiding 51% of attacks 75 . Consensus algorithms introduce various mechanisms that reduce the probability of 51% of attacks. However, improving network monitoring and malicious detections are essential to identify potential attack patterns, which trigger timely responses and avoid 51% of attacks. In machine learning, anomaly detection approaches are used to analyze the data and help detect suspicious activities that show the potential 51% of attacks 76 . Active network monitoring leads to the detection of attacks and ensures the security and integrity of the blockchain.

Impact on decentralization and consensus

The 51% of attacks can cause severe implications in the decentralization of the blockchain network. Hence, it should be considered as an immediate concern. Extensive research provides an understanding of the consequences of attacks on network governance, concentration power, and decision-making processes 77 . Decentralization shows that any single or group of entities does not control the network, immutability, resistance, and fostering transparency. However, a perfect 51% of attacks can allow an attacker to control the network and threaten decentralization. This power concentration contradicts decentralization, which introduces vulnerabilities and compromises the blockchain network's trust and integrity 78 . The existing consensus algorithms show the consensus reaching and validating the transactions . When a 51% attack occurs, the attacker can manipulate the consensus process, potentially invalidating transactions or reversing confirmed blocks. This disrupts the integrity of the consensus mechanism and raises concerns about the validity of the entire blockchain 79 . Research on the impact of 51% of attacks emphasizes the criticality of maintaining a decentralized network structure and robust consensus mechanisms. Efforts are directed toward developing countermeasures that promote decentralization and enhance the resilience of consensus protocols against such attacks 80 . Hybrid consensus models, for example, aim to combine multiple consensus algorithms to mitigate the vulnerabilities of individual approaches and achieve a more balanced and secure network. Safeguarding decentralization and consensus mechanisms also involves addressing factors such as governance and decision-making processes. Research explores ways to ensure fair and democratic governance structures where decision-making power is distributed among network participants 81 . Decentralized governance models prevent authority concentration with on-chain voting and transparent protocols.

In the real world, 51% of attacks serve as valuable resources for understanding the methodologies, impact, and responses associated with such attacks. Previous research has analyzed notable incidents, including attacks on Bitcoin Gold, Verge, and Ethereum Classic, to gain insights into the nature and consequences of these attacks 82 . By examining case studies, researchers can delve into attackers' specific techniques to gain majority control over the network's mining power. This analysis helps identify vulnerabilities within the consensus mechanisms and highlight areas where improvements are needed.

In recent years, many case studies have provided insights about the impacts of these attacks in the blockchain community, corresponding responses, and steps to reduce their effects 83 . Understanding the successful 51% of attacks can help to assess the economic losses, potential damage, and disintegration of user trust. This can be crucial for analyzing network participants to represent vulnerabilities, strengthen network security, evolve countermeasures, and provide active security strategies. Previous studies on 51% of attacks can provide the fundamentals to improve security, governance framework, developing mechanisms, monitoring, and detecting systems 84 . By gaining knowledge through these case studies, we can identify vulnerabilities, patterns, and best practices to develop more resilient blockchain networks. It is necessary to understand that the process of 51% of attacks can change frequently, leading to new attack vectors and the emergence of other techniques. Therefore, ongoing research and collaboration are essential to anticipate threats and address vulnerabilities associated with these attacks. The insights from case studies and real-world examples highlight the importance of continuous research efforts and interdisciplinary collaboration among researchers, developers, policymakers, and industry stakeholders. All these factors are combined to enhance security, trustworthiness, and resilience in the face of 51% attacks and other emerging threats to blockchain networks.

Consensus algorithms have several limitations that must be addressed to implement them effectively in real-world applications. Scalability is a significant concern, as many consensus algorithms need help to handle high transaction volumes and large network sizes. Finding efficient solutions to scale while maintaining security and decentralization is crucial for accommodating the demands of real-world applications. Energy efficiency is another limitation, especially in consensus algorithms like Proof of Work (PoW) that consume substantial energy. This not only makes them environmentally unfriendly but also economically unsustainable. Developing energy-efficient consensus algorithms is essential to reduce the carbon footprint associated with blockchain technology and ensure long-term viability.

Specific consensus algorithms introduce centralization risks, such as Proof of Stake (PoS) and Delegated Proof of Stake (DPoS). These algorithms can concentrate control in the hands of validators with more significant stakes or selected delegates, compromising the decentralization and trust that blockchain technology aims to provide. Balancing decentralization and stakeholder influence is crucial to maintaining a robust and inclusive network. Trust assumptions in consensus algorithms pose another limitation. Many algorithms rely on pre-approved validators or trusted authorities, which may not align with blockchain technology's decentralized and trustless nature. This limitation restricts the applicability of consensus algorithms in specific real-world applications that require higher levels of trust and security without relying on centralized entities.

Privacy and confidentiality are challenging within consensus algorithms prioritizing transparency and immutability. Striking a balance between data privacy and transparency is a complex task that consensus algorithms must address to protect sensitive information while maintaining the auditability and transparency required by various applications. Addressing these limitations in consensus algorithms will require continuous research, innovation, and collaboration between stakeholders in the blockchain community. By overcoming these challenges, consensus algorithms can unlock the full potential of blockchain technology in a wide range of real-world applications while ensuring scalability, energy efficiency, decentralization, trust, and privacy.

Machine learning techniques for security enhancement

Overview of machine learning algorithms applicable to blockchain security.

Machine learning algorithms offer a range of techniques that can be applied to enhance blockchain security. These algorithms leverage artificial intelligence and data analysis to detect anomalies, identify patterns, and make predictions, thereby strengthening the resilience of blockchain networks against potential security threats. This section discusses an overview of machine learning algorithms applicable to blockchain security.

Supervised learning algorithms

Supervised learning algorithms, such as Support Vector Machines (SVM) and Random Forests (RF), play a vital role in blockchain security by enabling classification tasks and bolstering the detection and prevention of fraudulent or malicious activities 85 . Support Vector Machines (SVM) stand out as a widely utilized supervised learning algorithm in the context of blockchain security. SVMs excel in binary classification tasks, where transactions must be categorized as legitimate or malicious. By creating a hyperplane that separates the two classes in a high-dimensional feature space, SVMs strive to find the optimal hyperplane that maximizes the separation of data points while minimizing classification errors. SVMs boast a robust theoretical foundation and are particularly effective in scenarios where the data is not linearly separable. Kernel functions manage the high-handling feature spaces more efficiently and achieve linear and non-linear classification tasks .

RF is an ensemble learning algorithm that utilizes multiple decision trees and performs predictions 86 . Each decision tree in this algorithm is trained on the data subset and uses random feature selections. The final predictions can be made by combining the individual tree predictions. RF has the potential to handle high-dimensional data and its robustness. This technique is effective in handling tasks that involve regression and classification. In blockchain networks, RF is highly effective in determining whether the transactions are legitimate or fraudulent through investigating features and patterns related to their security. RF can also detect malicious nodes by analyzing their interactions and behaviors.

Both SVM and RF offer distinct advantages in terms of performance and interpretability. SVMs are acclaimed for their adeptness in handling complex data and finding optimal decision boundaries, while RF excels in managing large and diverse datasets 24 . Both algorithms can deliver accurate and reliable results within blockchain security applications. It is important to note that the efficacy of these supervised learning algorithms relies on the quality and representativeness of the training data. Labeled datasets containing examples of legitimate and malicious transactions or nodes are crucial for effectively training the models 38 . Furthermore, feature engineering plays a critical role in extracting meaningful features from blockchain data, thereby enhancing the performance of these algorithms.

Unsupervised learning algorithms

Unsupervised learning algorithms such as clustering and anomaly detection will aid blockchain security by identifying threats and patterns without labeled training data. Clustering algorithms group similar transactions or network entities in blockchain security. Algorithms such as k-means or DBSCAN partition the data into clusters, each representing a group of similar data points 42 . Clustering algorithms can differentiate between regular and unusual behavior by examining the patterns in each cluster. This helps to identify security threats or suspicious activities in the blockchain network. This capability is precious when specific attacks or anomalies are unknown in advance, as clustering algorithms can unveil unknown patterns or groups within the data.

On the other hand, anomaly detection algorithms focus on identifying outliers or anomalies that significantly deviate from the expected behavior 87 . Algorithms such as Isolation Forest or One-Class SVM construct models of the expected behavior within the data and flag any instances that fall outside this norm. In blockchain security, anomaly detection algorithms can help identify unusual or suspicious transactions, network nodes, or activities that may indicate potential security threats, fraud, or network intrusions 85 . Identifying these irregularities will lead to rapid implementation of security measures, minimize the risk, and protect the blockchain network's dependability. Unsupervised learning can also provide valuable insights into the character and structure of blockchain data. This makes it feasible to detect the potential security risks and irregularities that may not have been previously labeled or identified. These algorithms can provide a comprehensive and proactive approach that enhances the capabilities of blockchain security. An unsupervised learning algorithm requires expert parametric tuning to perform precise and significant outcomes 84 . In cluster algorithm selection, several clusters or thresholds for anomaly detection can significantly influence the algorithm's efficiency. However, data preprocessing and feature engineering are essential in unsupervised learning that prepares the data, which relies on inherent structure and data distribution.

Deep learning algorithms

Deep learning algorithms such as CNNs and RNNs significantly influence blockchain security 88 . CNNs are highly effective in analyzing structured data like transaction graphs. Meanwhile, RNNs excel in analyzing sequential data like transaction histories and brilliant contract execution. These algorithms can effectively identify malicious and fraudulent activities by detecting anomalies and patterns. It can provide advantages such as autonomous learning, accurate anomaly detection, and handling large datasets.

Moreover, deep learning algorithms can continuously improve their performance, allowing them to adapt to new attack patterns and evolving security threats 89 . Nevertheless, it is crucial to consider that deep learning algorithms require substantial amounts of labeled training data and significant computational resources to train and deploy effectively. Model interpretability and explainability can be challenging with deep learning models, given their complex nature and operation as black boxes. Ensuring the privacy and security of sensitive blockchain data during the training process is also a critical consideration 90 . By integrating deep learning algorithms, such as CNNs and RNNs, into blockchain security frameworks, researchers can enhance threat detection and strengthen the trustworthiness and resilience of blockchain networks.

Reinforcement learning

Reinforcement Learning (RL) is a valuable branch of machine learning that enables the training of intelligent agents to make sequential decisions to maximize cumulative rewards 91 . RL algorithms like Q-learning or DQNs can help create intelligent agents to secure blockchain with optimal security policies. Blockchain agents boost security by verifying blocks, selecting consensus methods, and preventing attacks. Through interactions with the blockchain network, RL agents observe the current state, take actions, and receive rewards or penalties based on the outcomes of their activities, thereby learning from their experiences during training 92 . RL agents explore the environment during training and learn through trial and error. The goal is to obtain a policy that increases rewards over time by enhancing security measures and reducing potential threats to blockchain security. RL empowers blockchain networks with adaptive and intelligent decision-making capabilities. RL agents can learn to identify attack patterns, anticipate vulnerabilities, and respond to emerging threats in real-time 93 . This adaptability is particularly valuable in blockchain security's dynamic and evolving landscape, where new attack vectors and vulnerabilities continually occur.

Furthermore, RL algorithms can be combined with other techniques like supervised or unsupervised learning to enhance the learning process. Pre-training RL agents with previous data or labeled examples improves their learning speed and performance in new situations. However, deploying RL algorithms in blockchain security presents challenges 94 . One critical challenge involves defining an appropriate reward structure that accurately reflects the security objectives of the blockchain network. RL agents must balance exploring new security measures and relying on established ones by finding a balance between exploring new actions and exploiting known strategies.

Bayesian networks

This algorithm uses probabilistic reasoning, which helps the system model and analyze the elemental correlations between the variables and blockchain security. It shows uncertain and incomplete information with directed acyclic graphs 95 . The calculations are performed for the event probability based on their observed evidence by quantifying conditional variable dependencies and probabilities. The Bayesian network provides valuable information, such as a blockchain network's security risks and vulnerabilities, and enables stakeholders to act appropriately 96 . By incorporating additional communication and handling missing or incomplete data with probabilistic reasoning, this algorithm is flexible and quickly adapted to changing the real-world scenario.

Generative adversarial networks (GANs)

In a blockchain network, GANs are a vital tool to enhance security in distributed networks. It has two components, a generator, and a discriminator, that create artificial datasets that replicate the real-world data exactly. GANs provide several benefits, such as creating diverse, realistic data for attacking scenarios 97 . This approach can simulate the attackers' actions and improve the network's defense system. However, this model can also enhance the anomaly detection system, improving blockchain security . In order to train the GANs, enormous computation resources and extensive training data sets are required 98 . Furthermore, it requires high-quality and diverse datasets, which influence the reliability of the synthetic data and improve the performance. Even though it has limitations, implementing GANs in blockchain provides real-world opportunities for developing attacking scenarios, testing blockchain security, and performing an effective anomaly detection system.

Privacy-preserving techniques (PPTs)

This technique has two security components, namely Differential Privacy (DP) and Secure Multi-Party Computation (SMPC). These components are necessary for maintaining confidentiality and anonymity in the distributed blockchain network 99 . DP can enable control over the noise, data perturbation, and query response, which avoids data point identification. However, SMPC works on multiple parts that collaborate with ML tasks and computations and improve privacy. Combining these approaches, PPTs can ensure the safety of the applicant's data and compliance with data protection regulations. Furthermore, it can also enable the detection of anomalies and prevent malicious activities 100 . While selecting the appropriate methods, we should consider the requirements such as privacy level, data type, analysis or prediction task, and computational overhead.

Feature extraction and anomaly detection for 51% attack prevention

As a researcher, it is imperative to highlight the significance of feature extraction and anomaly detection in preventing 51% of attacks in blockchain networks. These techniques are pivotal in identifying and flagging abnormal patterns or behaviors that may signify potential attacks, allowing prompt intervention to mitigate associated risks. By delving into feature extraction and anomaly detection details, we can gain deeper insights into their crucial role in preventing 51% of attacks in blockchain networks.

Feature extraction

Analyzing blockchain data requires feature extraction, which is crucial in identifying and extracting meaningful information that captures the essential characteristics of transactions, network behavior, and participant activities 101 . Analysts obtain meaningful insights such as the blockchain network's security, performance, and efficiency, enabling information-based decision-making. Transaction-based features provide insights into individual transactions, encompassing transaction size, frequency, time stamp, input–output ratios, graph properties, and volumes 36 . Furthermore, these functions expedite the anomalies, detecting the malicious transactions and recognizing patterns associated with the malicious behavior.

The behavior, connectivity patterns, centrality, and consensus participation of blockchain are analyzed through network-based features. This analysis helps evaluate the network structure, identifies the significant nodes that influence it, and identifies any abnormal network activity 98 . Participant-based features center on individual participants within the blockchain network, encompassing reputation scores, stake sizes, and consensus participation history. These features assess the trustworthiness of participants, detect potential malicious actors, and evaluate the reliability of network contributors 102 . Smart contract-based features involve the analysis of smart contract code and properties, allowing vulnerability assessments, identification of attack vectors, and monitoring for suspicious behaviors during contract execution 59 .

Feature extraction is the foundation for subsequent analysis and modeling tasks in blockchain analysis. ML algorithms acquire the extracted features and perform statistical analyses. Researchers can gain insights that help to identify the pattern and make formal decisions based on these inputs. The feature selection always depends on the data under examination in blockchain and meets the goal, which helps the researchers better understand the blockchain network.

Anomaly detection

Detecting anomalies is critical, which secures the blockchain and maintains integrity. The process involves the identification of deviations that lead to potential attacks, abnormalities, and fraudulent activities. Irregularities can be identified using graph-based methods, behavioral analysis, ML, and statistical techniques 103 . Statistical approaches utilize statistical techniques to identify abnormalities in transaction patterns, network behavior, or consensus parameters. Clustering techniques group similar transactions or network entities to identify outliers or unusual patterns 98 . Outlier detection methods focus on identifying data points that significantly deviate from the expected distribution. Time-series analysis detects anomalies in temporal patterns or trends, enabling the identification of abnormal behaviors over time.

Machine learning approaches are impressive for anomaly detection in blockchain networks. Unsupervised learning algorithms automatically identify anomalies based on patterns and statistical deviations from normal behavior. These algorithms learn from previous data, detect subtle changes, and adapt to evolving attack patterns. Supervised learning algorithms can also be utilized with labeled data to classify instances as normal or anomalous based on known patterns 23 . Graph-based approaches leverage the structure of the blockchain network to identify anomalies. Graph analysis techniques detect changes in node centrality measures, unexpected or suspicious connections, or alterations in community structures. Analyzing the network graph can detect malicious activity like changes in consensus participation or new connections 36 .

The behavioral analysis focuses on establishing normal behavior profiles based on previous data. Analyzing transaction patterns, network interactions, or consensus participation can establish a baseline of expected behavior 104 . Deviations from these profiles, such as sudden changes in transaction volumes, irregular consensus participation, or unusual network interactions, are flagged as anomalies. Threshold-based approaches involve setting thresholds for specific features or behaviors and monitoring deviations beyond those thresholds. For example, exceeding a certain threshold in transaction volume or encountering a predetermined limit of consensus failures may indicate an anomaly 105 . These approaches are straightforward to implement and provide an early warning system for potential anomalies. By utilizing these approaches, researchers can enhance the security and robustness of blockchain networks by promptly detecting and mitigating anomalies.

Real-time monitoring and alerting

Real-time monitoring and alerting are integral components of a robust anomaly detection system in blockchain networks. The ability to detect anomalies in real-time and promptly respond to potential cyber-attacks is necessary for upholding network security and integrity 90 . Real-time monitoring ensures that any abnormal behavior or suspicious activities are swiftly identified and addressed, thereby minimizing the potential effect of attacks. Continuous analysis of blockchain data through ongoing anomaly detection methods is essential for real-time monitoring. As discussed previously, machine-learning models can be trained to detect anomalies in real-time and deployed to analyze incoming data streams for deviations from normal behavior or expected patterns 84 . The system uses machine-learning algorithms to identify and alert users of potential attacks or malicious activities as soon as they are detected. Automatic alerts are challenging to identify quickly and immediately respond to anomalies. This information can be sent through email, SMS, or mentioned on a dashboard. Integrating the ML model with the defined framework process can lead to real-time monitoring. Developing well-defined responding mechanisms involving active protocols isolating the compromised nodes and enabling additional security measures is necessitated 106 .

Dynamic adaptation

Dynamic techniques lead the blockchain network to adopt 51% of attacks that detect and respond to emerging threats. The continuous learning process in ML models can analyze the attacking pattern, detect new threats, prevent potential attacks, and improve accuracy 107 . This never-ending learning makes the system practical for identifying potential security risks. Feedback loops are critical for effective functioning. It can provide valuable information about false positives and negatives, improving anomaly detection accuracy, refining thresholds, and enhancing overall performance 100 . Dynamic adaption made the adjustments by consensus parameters or security measures and mitigated the potential attacks based on anomaly detection. Network configuration can be modified, limiting the action of malicious transactions or nodes and preventing possible attacks. Feature extraction is a critical factor in enabling dynamic adaptation and minimizing the effects of the attacks 108 . The system can identify the characteristics by extracting valuable information from blockchain transactions. This is more valuable to detect anomalies and identify deviations from the patterns. This technique will be updated based on new attacks and includes the latest indicators and behaviors related to 51% of attacks.

Machine learning-based consensus decision-making

ML-based consensus hybridization is a promising method that enhances blockchain networks' security and trust. ML Techniques such as data analysis and pattern recognition can improve the network mechanisms. Intelligent decision-making is performed by leveraging ML algorithms. By investigating the previous data, ML models can collect the essential information that predicts the effects of various consensus performance parameters. This leads to optimizing the consensus parameters such as block size, difficulty level, and adaption over the change in network and improving scalability. In addition, ML-based consensus decision-making commits to blockchain security by implementing the identification, detection, and mitigation of suspicious attacks. ML models monitor the consensus process, detect anomalies, perform more proactive measures, and serve the network's integrity. Furthermore, this method also attains the potential to recast the consensus approach toward data availability, interpretability, privacy, and governance. This section discusses the different factors and their influence on the ML-based consensus decision-making process.

Consensus parameter optimization

Consensus parametric optimization is required to develop a high-performance, secure, and scalable blockchain network. ML algorithms optimize these parameters using the previous data and real-time network conditions. These models can investigate the previous data, which helps to identify the patterns and trends of the network performances through block size, difficult adjustment, time, and validation rules. ML model can identify the different parameters affecting the network's performance and optimize values that improve the efficiency of blockchain operations. In real-time scenarios, monitoring networking conditions allows ML models to adopt the dynamic consensus parameters, which examines the network metrics and provides necessary modification over the consensus mechanisms. These models provide valid suggestions to increase the block size and decrease block time, enhancing scalability in higher transactions. However, when the network faces any security threats, the proposed model learns from experience and recommends changing the difficult level or other validation rules and adapting consensus parameters to improve security. The proposed model can analyze the previous data and understand how the parametric setting affects the network performance and behavior. This learning model will refine the proposed system, which will result in improving the accuracy and optimizing the performance. Through ML, optimizing consensus parameters can improve network performance and security while also improving blockchain systems' scalability and adaptability. ML models can adjust the consensus parameters as the network changes to fit the new conditions, workloads, and threat levels. Adaptability allows the blockchain network to maintain its efficiency, security, and resilience even when faced with dynamic and changing environments.

Adaptive consensus selection

Blockchain networks need a suitable consensus algorithm that adapts to their dynamic conditions and needs. By analyzing past data and network metrics, ML is vital in choosing the best consensus algorithm. ML models can analyze performance metrics of various consensus mechanisms, such as transaction throughput and confirmation latency. These analyses can identify patterns and trends that highlight the strengths and weaknesses of each algorithm in specific network environments. Real-time monitoring allows the machine learning models to make adaptive decisions regarding consensus algorithm selection. The models constantly analyze network load, security needs, and node capabilities to select the best algorithm for the current situation. For instance, during heavy traffic, the system may choose a consensus algorithm that increases transaction speed. However, if security is the top priority, a Byzantine fault-tolerant algorithm may be selected instead. The ML models learn from previous data and adapt to changing conditions, improving their decision-making accuracy. Adaptive consensus selection brings numerous benefits to blockchain networks. Our system carefully selects the best consensus algorithm for every situation to maximize network efficiency, ensuring top-notch performance and resource utilization. Another important aspect is that it boosts network security by adapting the consensus algorithm according to the current security needs and potential threats. The network can protect itself against attacks and preserve the integrity of the blockchain system by adjusting to the situation.

Intelligent block validation

Ensuring the safety and accuracy of a blockchain network relies heavily on intelligent block validation. ML models can play a crucial role in this process. ML algorithms can help to make informed decisions about block validation by analyzing transaction data, network behavior, and consensus rules. By analyzing validated blocks from the past, these algorithms can detect patterns that signify valid transactions. This helps them develop a thorough understanding of a legitimate transaction. ML models categorize the valid and invalid blocks, improving block validation. This procedure can reduce manual inspection and detect malicious activities, improving efficiency in data transactions and network behavior. ML models can correctly identify potential threats, which leads to learning and evolving from the newly arrived data. This introduces effective adaption, changes the network conditions, and elevates themselves from attacking strategies. Furthermore, intelligent block validation improves security and confirms the valid blocks added to the blockchain.

Fraud detection and prevention

Blockchain networks can utilize the ML model and sophisticated algorithms, which analyze transaction patterns and network behavior and detect and prevent malicious activities effectively. The ML model analyzes previous data and detects unusual patterns and behaviors identified as fraudulent activity precisely and accurately . This makes the ML tools more powerful to maintain security and integrity. The proposed system uses supervised learning approaches and trains on labeled data to accurately classify whether the transactions are valid. This method also considers parameters such as transaction amount, time stamps, network interaction, and user behavior for exact predictions. Unsupervised learning methods can analyze the transaction pattern and network behavior, identify deviations from the normal ones, and finally detect anomalies in the blockchain data. This method detects fraud and unknown practices more effectively and identifies the double-spending or Sybil attacks as fraud attempts .

Furthermore, Natural language processing (NLP) techniques are used to analyze the textual data, feedback, and forum discussions to detect potential vulnerabilities. Keeping ML algorithms up-to-date and learning continuously from the new data will always recognize the patterns and prevent fraudulent activities. Earlier detection can reduce potential damage and enable high security. Hybridization of ML and Blockchain develops the network into more trustworthy and resilient.

Predictive analytics for consensus optimization

Predictive analytics are essential and enhance consensus mechanisms. ML models can analyze previous data and patterns, forecast network conditions, and optimize consensus processes. This model goes through different situations and predicts the network behavior by examining network latency, resource usage, and transaction throughput. This leads to the proposed model's ability to perform proactive, intelligent decision-making, which optimizes the consensus mechanisms. Predictive analysis can offer intelligent parametric optimization and adaptive consensus selection, which is anticipated to mitigate security threats. This allows the proposed system design to perform efficient and adaptable operations of the blockchain network under dynamic workloads and varying environments. Predictive analytics is useful for detecting and preventing fraud. Machine learning models can identify patterns of fraudulent activity, which can then trigger alerts and preventive measures for participants in the network. This proactive approach helps maintain the blockchain network's integrity and trustworthiness.

Research methodology and materials

This section will discuss the proposed architecture's steps, components, and modules. The proposed architecture is developed using the ProximaX blockchain infrastructure platform that combines blockchain with the distributed service layers. This integrates blockchain networks with decentralized storage, database, streaming, and enhanced smart contract services to create an all-in-one user-friendly platform. ProximaX is designed to achieve high scalability, and throughput provides low latency. This platform is available in private, public, and consortium configurations and accommodates additional services without compromising the performance. This unique platform is built on reliable technologies that can be used in all industry segments. Researchers and practitioners can easily design and develop an application on a secure with high availability at a low cost.

The proposed architecture combines ML approaches with consensus to achieve agreement in a blockchain network. Integrating ML algorithms with consensus mechanisms can significantly enhance decision-making in distributed systems. These hybrid algorithms aim to address the limitations of consensus protocols by utilizing ML models. The algorithm gathers important data, extracts meaningful features, and trains ML models with data from the system model. These models are then incorporated into the consensus protocol to optimize decision-making, improve security through anomaly detection, and enable adaptive learning. Figure  2 provides a visual representation of how ML models are integrated with hybrid consensus algorithms in a ProximaX blockchain network to enhance efficiency, scalability, and fault tolerance while ensuring the integrity of the consensus process through the prediction model.

Proposed system using blockchain and machine learning layer.

Initially, the module is developed for collecting and extracting the necessary information from the ProximaX blockchain network. Next, the feature extraction module processes the data to extract meaningful features that capture pertinent information for consensus. The ML training module then uses various algorithms to train models based on the extracted features. The anomaly detection module analyzes incoming data using the trained ML models to identify abnormal behaviors or attacks. If any anomalies or attacks are detected, the consensus decision-making module evaluates them, assesses their impact, and determines appropriate actions to maintain consensus integrity. Finally, the consensus enforcement module ensures that the decisions made by the consensus decision-making module are enforced within the network. This iterative process involves continuous feedback, where data is collected, features are extracted, models are trained, anomalies and attacks are detected, consensus decisions are made, and enforcement is carried out. This enables the consensus architecture to adapt to changing network conditions, identify anomalies or attacks, and maintain consensus integrity based on intelligent ML decision-making. Figure  3 shows the design flow of the proposed work. The detailed discussion over the methodology involved in this research is discussed below through step-by-step analysis.

Design flow diagram for the proposed work.

Review and identify the attack scenarios : The first step is reviewing existing consensus mechanisms and analyzing their limitations in required applications. Additionally, it identifies the different attacks that can occur in an application using blockchain. This information can be used to create a set of labeled data that can be used to train the machine learning algorithms.

Choose a consensus algorithm : The second step is to choose an appropriate consensus algorithm that meets the specific requirements of the blockchain-based system. Here, the ProximaX-based blockchain environment is considered. Hybrid consensus algorithms, which combine elements of different consensus algorithms, can provide robustness and security to the blockchain system. This can help prevent cyber-attacks, such as 51% of attacks and double-spending attacks. This algorithm will ensure the integrity and reliability of the blockchain. Some of the hybridizations of consensus algorithms discussed in this research work are listed below.

Delegated proof of stake work (DPoSW) : DPoS validates blocks in the blockchain while PoW creates them, making it harder for attackers to manipulate the network. It uses a limited number of elected validators, enabling faster block confirmation times and higher transaction throughput than PoW. However, DPoS comes with the risk of centralization and relies on trust in elected representatives, which can compromise decentralization. These factors should be considered when evaluating consensus algorithms.

Proof of stake and work (PoSW) : Blockchain uses PoS and PoW to enhance security and efficiency. PoW increases security against attacks, while PoS allows for better energy efficiency and scalability. However, PoS can lead to power concentration, requiring mechanisms to address the "nothing at stake" problem. Balancing decentralization and efficiency involves trade-offs. PoS creates governance complexity and requires careful management for transparency and inclusivity. Long-term security is a consideration as reliance on PoS increases and PoW decreases.

Proof of CASBFT : Casper-PBFT is a hybrid consensus algorithm that combines Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to improve network security and transaction speed. It offers strong consistency, rapid transaction finality, and scalability advantages. However, careful selection and governance of validators are necessary to avoid centralization risks, and the initial stake distribution may lead to power imbalances. Proper design, testing, and maintenance are necessary due to increased complexity. Adoption should be based on network requirements and associated trade-off management.

Delegated byzantine proof of stake (DBPoS) : A secure and scalable system is achieved through a DPoS-PBFT hybrid algorithm. DPoS elects delegates for faster block confirmation and increased transaction throughput, while PBFT enhances resilience against failures. However, DPoS's small delegate set may lead to centralization and collusion risks, requiring proper governance and transparency. Careful design and monitoring are necessary to balance decentralization and efficiency. DPoS may affect decentralization compared to PBFT.

Choose machine-learning algorithms : Select ML algorithms, such as supervised, unsupervised, or rule-based learning, suitable for detecting and responding to attacks.

Steps involved in ML : ML approaches can be used to help prevent attacks on blockchain-based applications by:

Anomaly detection : ML algorithms can identify and flag unusual network behavior, allowing network participants to quickly detect and respond to potential attacks.

Prediction modeling : Predictive models can be trained to identify the likelihood of an attack based on previous data, allowing network participants to take preventative measures proactively.

Clustering and classification : Clustering and classification algorithms can be used to identify and categorize different attacks, making them easier to understand.

Network traffic analysis : Machine-learning algorithms can analyze network traffic and identify patterns that indicate potential attacks, allowing network participants to respond quickly.

Blockchain data analysis : Machine-learning algorithms can be used to analyze the data stored on the blockchain, such as transaction history and network activity, to identify potential attacks.

Fraud detection : ML algorithms can detect fraudulent transactions, such as double-spending or fake transactions, and prevent them from being added to the blockchain.

Risk assessment : ML algorithms can be used to assess the risk posed by different nodes on the network and prioritize security measures based on the risk level.

Reinforcement learning : ML algorithms can learn from network interactions and optimize the security measures to respond to potential attacks.

Data collection : Collect data from the required system to train the machine learning algorithms. This data should include normal and abnormal behavior patterns.

Train machine-learning models : Collect and label data from the system to train machine-learning algorithms, such as supervised, unsupervised, or rule-based learning, to detect and respond to cyber-attacks. This will help the algorithms identify abnormal behavior patterns that indicate an attack.

Integrate the consensus algorithm and machine learning models : Integrate the consensus algorithm and the machine learning models into the blockchain-based system such that the machine learning algorithms can identify and trigger a response mechanism through the consensus algorithm.

Implementing ML in the blockchain : Responsible machine learning (ML) involves developing, deploying, and using ML models that are ethical and accountable. This includes fairness, transparency, interpretability, and privacy. To achieve this, avoid biases and discrimination in data collection and model training, document decisions and assumptions, interpret predictions, and implement privacy safeguards. The ML life cycle includes formulation, acquisition, development, testing, deployment, and ongoing monitoring and maintenance. Responsible ML ensures the trustworthy and ethical use of ML technologies.

Performance metrics : To evaluate a machine-learning model, use metrics like Confusion Matrix, Accuracy, Precision, F1 Score, R-squared, ROC Curve, Area under ROC Curve, and Goodness of Fit. Analyzing these metrics helps identify areas for improvement and determine if the model suits the production environment.

Monitor the system : Continuously monitor the proposed system application and update the machine learning algorithms and consensus algorithm to adapt to evolving attack patterns.

Respond to an attack : If an attack is detected, the machine learning algorithms will trigger a response mechanism that is determined by the consensus algorithm. The response mechanism may include limiting the attacker's access, rolling back the blockchain, or triggering a secure emergency shutdown.

Test and evaluate : Test the hybrid system in a controlled environment to assess its effectiveness in detecting and responding to cyber-attacks.

Deploy : Once the system has been tested and evaluated, deploy the hybrid consensus algorithm and machine learning approach in the real-world environment.

Monitor and update : Continuously monitor the system for performance and security and update the ML and consensus algorithms to ensure their effectiveness against evolving attack patterns. However, implementing a hybrid consensus algorithm with an ML approach requires choosing an appropriate consensus algorithm, training ML models, integrating the consensus algorithm and ML models, testing and evaluating the system, and deploying the system.

Experimental results and discussion

Figure  4 shows the experimental diagram for this research work. In an IoT environment, obtaining vital information by deploying sensors that can detect various parameters from real-world scenarios is difficult. Establishing a robust system to collect this data in real-time or at regular intervals is paramount. Additionally, it is necessary to ensure the structured and balanced data and keep track of the time it was collected. Advanced techniques can be used to obtain valuable insights from the data. When training machine learning models, selecting and combining the most relevant features with labeled data is essential to building a training dataset.

Experimental test setup with intelligence analysis.

Furthermore, correct labeling of the target variable for machine learning is necessary. When setting up a Blockchain network, it is essential to choose a proposed consensus algorithm that fits the network's needs, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT) Delegated Byzantine Proof of Stake (DBPoS). This algorithm determines how nodes agree on transactions and adds new blocks to the blockchain. Collecting data from the network is required to detect potential threats. Information like transactions, block details, timestamps, and network states help us stay keen and secure. Relevant features related to transaction volume, block size, transaction fees, or other parameters should be extracted to identify potential attacks. Feature extraction techniques can analyze transaction patterns, mining activities, block propagation delays, or network connectivity measures. Anomaly detection algorithms, such as statistical methods, machine learning techniques, or graph-based analysis, can be used to identify abnormal patterns or behaviors. If an anomaly is detected, it should be labeled as a specific type of attack, such as double-spending, selfish mining, Sybil attacks, or 51% attacks. Ensuring equal representation of typical and attack instances in the training dataset is crucial to boosting the model's performance. This approach is necessary to prevent bias and establish the proposed model to achieve the user's requirement.

After preparing the model, it can be implemented into an IoT environment to analyze future data or make predictions in real-time. In order to make decisions fast and accurately, it is necessary to integrate the model coherently with the current IoT infrastructure . Meanwhile, deploying the trained model in the blockchain network can help to detect and monitor potential attacks in real time. Integrating the model into the blockchain infrastructure enables proactive defense mechanisms against malicious activities. After preparing the model, it can be implemented into an IoT environment to analyze future data or make predictions in real-time. Integrating the model with the existing IoT infrastructure is necessitated to ensure efficient and effective decision-making. To ensure strong security measures, continuously monitor and improve the model by gathering new data and adapting to emerging attack patterns.

Improving consensus algorithms' performance depends on factors like block confirmation time, transaction throughput, energy efficiency, latency, scalability, and fault tolerance. In this work, we can enhance the algorithm's performance by fine-tuning its parameters using optimization algorithms, grid search, or random search techniques. They can then evaluate the optimal parameter values using metrics that reflect the desired performance factors. By comparing different parameter configurations, we can choose the best values that maximize the desired performance factors. By implementing this methodology, we anticipate a more promising and enhanced efficacy within blockchain technology. In blockchain networks, various changes occur, such as network size fluctuations, workload, latency, or adversarial activities. An adaptive consensus algorithm that can adjust its behavior as needed is required to achieve optimal performance. Our work involves developing rule-based mechanisms that dictate the circumstances in which the algorithm should adjust. These guidelines can be determined by various factors such as network parameters, performance metrics, or system-level thresholds. By adhering to these conditions, the algorithm can adapt to changing network conditions by modifying its mode or parameters. Machine learning or reinforcement learning techniques can be leveraged for intelligent adaptive selection. By training models on previous data and network conditions, algorithms can autonomously adapt based on learned patterns or reinforcement learning rewards and penalties. It is necessary to consider the preferences and needs of the users during the adaptive selection procedure. Consensus algorithms are crafted by integrating user-specified criteria that align with the unique requirements of the blockchain application, including but not limited to scalability, security, and energy efficiency.

Understanding the needs of stakeholders, identifying the priorities, and considering specific applications are essential for developing an adoptable ML-based blockchain network. Integrating Consensus Algorithms with ML approaches attains the potential to optimize the network performance and effectively respond to the user's requirements. Stakeholders may possess transaction throughput, privacy, decentralization, energy efficiency, or consensus speed-based priorities. Analyzing these requirements will introduce intelligent selection, which selects hybrid consensus algorithms such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), and Delegated Byzantine Proof of Stake (DBPoS). The proposed algorithms have their strengths and trade-offs in security, scalability, efficiency, and decentralization. However, these performance factors are determined by finding the required algorithms based on the user's requirements.

Optimization of the consensus algorithm requires the adjustment of the network parameters based on the user requirements. This can be achieved through several optimization techniques. Here, an ML-based blockchain network has been developed to monitor the network's performance and allows the system to make an adaptive decision based on the feedback from the real-world scenario. Continuous monitoring, evaluation, model refinements with periodic assessments, experimentation based on stakeholders' responses, and user feedback are highly required to implement successful models. Valuable insights are gained by performing the investigation using ProximaX, and results are expected to overcome the different types of attacks in the proposed work. The expected results are listed below.

Real-time attack detection and response,

Maintenance of blockchain integrity,

The smooth operation of a proposed application,

Adaptability to evolving threats,

Improved security,

Minimized attack damage,

Efficient defense mechanism,

Increased trust in the system, adoption, and better performance.

The prime objective of this research work is to identify and respond to the attacks in real-time scenarios immediately. This can be achieved by implementing the ML model to determine the paranormal behavioral patterns that act as a threat. This improves the security and resilience of the blockchain network and minimizes the damage. Maintaining the integrity of the blockchain network is another important objective. Consensus algorithms can validate the transaction and ensure the reliability and stability of the Blockchain network. By combining these Consensus algorithms with a hybrid approach, the proposed system attains higher transactional data integrity and lowers unauthorized activities. ML model and Hybrid consensus mechanisms are combined to ensure convenient operations. These results in highly comprehensive defense mechanisms. This approach can reduce the downtime risk and provide effective operations. However, this approach is adaptable to an evolving new threat and maintains reliable and efficient operations in a specific application-oriented system. The proposed ML models can be kept updated and customized to encounter emerging attacks, which performs the guaranteed defense mechanism for real-world environments.

This approach can proactively identify, detect, respond to, and mitigate the threats. However, it can also reduce the system vulnerability and damages caused by the threats. Expeditious measures are performed to protect the stability and functionality of the network, ensuring better system operations. The proposed hybrid approaches can efficiently protect the blockchain-based system against attacks. The proposed method adapts and updates continuously, which performs fine-tuning to understand emerging threats. This also enhances trust and encourages adaptability in intelligent grid sectors. These features can increase the stakeholder's confidence and improve system performance, resulting in stability, security, and efficient defense against attacks.

Security enhancements achieved through the proposed solution

By combining ML techniques with consensus protocols, the blockchain network performs anomaly detection, adaptive decision-making, and detection of malicious activities. These techniques can detect and mitigate various cyber-attacks by continuous network monitoring and analyzing real-time datasets. However, this hybrid approach provides robust security enhancements that enhance decision-making. This section discusses the various security enhancements achieved through the proposed methodology.

Increased attack resistance : Incorporating ML techniques and hybrid consensus protocols in blockchain networks increases the attack's resistance. This identifies and prevents attacks such as 51%of attacks, Sybil, and double spending attacks. ML models can detect potential attacks by collecting transaction details, network connectivity, and participant behavior. However, the proposed method ensures network security and integrity. This ML model analyzes the network behavior and transaction patterns using previous data obtained from the network, which helps to detect malicious activities and perform precautionary measures. This can prevent almost half of the attacks, detect malicious mining behaviors, and immediately send alert information to prevent attacks. These proactive techniques will minimize the negative impacts of the blockchain network and maintain high security, which is appreciable for the stakeholders and network participants.

Furthermore, the ML model can prevent Sybil and double spending attacks on blockchain networks. These models can use the previous network information and detect irregularities, flags, and fraudulent activities. Combining ML algorithms with a hybrid consensus process will improve the network's ability, promoting confidence in the blockchain network and protecting the stakeholder's assets and transaction details.

Dynamic threat detection : ML algorithms play a crucial role in detecting new threats and identifying the attacking patterns in the blockchain network. The proposed ML model can identify the unusual behaviors and patterns that represent potential attacks by analyzing the datasets from the existing networks. In addition, continuous learning in ML techniques leads to the immediate adaption of new approaches used by malicious attackers, which develop the network into highly secure. These algorithms can detect attacks, including DDoS, Network intrusions, data breaches, and malware propagation. ML model can perform dynamic threat detection, which enhances blockchain security and promotes trust among the network participants and stakeholders.

Anomaly detection and prevention : Effective anomaly detection and introducing preventive measures lead to maintaining the high security and integrity of blockchain networks. ML models are irreplaceable tools that detect mischievous transaction behavior, network connectivity, and other participant activities. It can perform proactive detection and prevent suspicious activities. Analyzing transaction records, network logs, participant interactions, and other system parameters leads the ML model to learn the standard patterns and behaviors exhibited within the network. However, these models establish the expected baseline and detect the deviations from these patterns that indicate fraudulent activities. ML techniques such as clustering, outlier detection, time-series analysis, and graph-based techniques are used to identify potential attacks. Integrating these techniques with blockchain networks can enhance proactive identification, which detects malicious activities in real time.

Furthermore, anomaly detection can deter potential attackers and discourage fraudulent activities . This can improve the confidence level of the stakeholders and participants and promote the adoption of blockchain technology. The proposed ML model can integrate the network decision-making process. For instance, detecting anomalies leads the network to trigger additional verification automatically and employs priority-based security over efficiency. This adaptive response makes the system resilient, secure, and trustworthy to face potential threats.

Adaptive decision-making : ML models facilitate the blockchain networks based on previous data in real time and adapt the decision-making process. This systematic approach can improve performance and optimize security by investigating network congestion and resource availability. We can analyze the metrics and adjust the consensus parameters model with ML approaches. This leads to block time and size variations that improve the transaction throughput and network performance in congested networks. Similarly, for detecting malicious behavior, the proposed model suggests actions such as improving validation regulations and tweaking the consensus protocols. These models can be trained based on previous data, which performs the adaptive choices to enhance security. It can identify patterns or trends that affect network performance and security by analyzing experiences. This knowledge allows the models to make informed decisions and adjust the consensus parameters accordingly. Adaptive decision-making is essential for blockchain networks because it enables quick response to changes by adjusting real-time consensus parameters. It also enhances security by detecting and responding to emerging threats using ML models. This approach optimizes resource allocation, improving efficiency, scalability, and network participation. Continuously adapting decision-making strategies ensures the network remains responsive to emerging challenges and continually improves performance, security, and resilience.

Robust network monitoring : The safety and stability of blockchain networks heavily rely on dependable monitoring. Hybrid consensus protocols use ML to continuously monitor and analyze the network, detecting security threats, attacks, and irregular behaviors. ML algorithms are particularly effective at identifying patterns and anomalies in large amounts of data, such as traffic and transaction data. Monitoring the blockchain network in real-world scenarios provides earlier warnings and other appropriate trigger responses. Robust monitoring can detect critical behavior, potential attacks such as DDoS, Sybil, tampering with the data transactions records, and other security breaches. This also uncovers the security vulnerability and pattern with exploitable loopholes, mitigates the attacks, and maintains the network integrity. ML models address these issues proactively and improve the system's ability to fight security threats and enhance overall network security. Furthermore, these models can identify recurring patterns and adjust their response strategies to improve outcomes in strengthening high adaptability. By using federated learning and homomorphic encryption, consensus mechanisms can detect anomalies and security breaches, improving the privacy and security of the entire blockchain network.

Scalability, decentralization, and energy efficiency considerations

The hybridization of consensus algorithms and ML models addresses challenges such as scalability, decentralization, and energy efficiency in distributed systems. This approach can disseminate the workload across various machines, perform effective data processing, and execute parallel algorithms. The decentralization process can be achieved using P2P networks, which improves reliability and prevents failures. To ensure data availability and integrity, redundancy and replication techniques like sharding or erasure coding can be used, even in node failures. Energy efficiency has become a critical concern as the demand for sustainable computing solutions grows. Consensus algorithms and ML models should be hybridized while considering energy efficiency to minimize environmental impact and operating costs. Use specialized hardware or cloud services to save energy when performing resource-intensive tasks like machine learning or complex consensus computations. These specialized infrastructures are designed to maximize computational efficiency, reducing energy requirements. Developing consensus algorithms with energy efficiency in mind can also minimize computational and communication overhead. PoS consumes less energy than PoW algorithms, relying on stakeholder voting rather than resource-intensive mining. ML models can be compressed or quantized using techniques like pruning, quantization, and knowledge distillation to reduce their size and energy consumption without sacrificing accuracy. Hybridizing consensus algorithms and machine learning models can address distributed systems' scalability, decentralization, and energy efficiency challenges. Considering these factors, we can develop sustainable solutions that leverage the best of both worlds.

Advantages and optimizations achieved through the proposed approach

Hybrid consensus algorithms and ML approaches are widely used to overcome various attacks in blockchain technology. The advantages and optimizations achieved through the proposed approaches are listed below. The key insights and SWOT analysis of the proposed research work are shown in Fig.  5

SWOT analysis of the hybrid approach.

Improved security

Advanced security is necessary for safe and reliable blockchain network systems. Practical ML techniques with hybrid consensus create a secured network with high integrity and reliability, preventing potential attacks and negative impacts. This model can establish a trust-based framework that ensures the network nodes perform valid transactions and avoid unauthorized attackers. Furthermore, some attacks require other measures that represent abnormal behavior. ML techniques can investigate substantial data volumes, identify patterns, and detect malicious attacks in real time. Here, continuous monitoring of energy transactions, communication patterns, and other system parameters is performed by integrating ML techniques. The system can utilize the previous data and effectively identify the unusual activity of ongoing attacks. Combining consensus and ML algorithms can create a robust defense mechanism and prevent cyber-attacks. Integrating ML techniques leads the system to detect and prevent the attack before any damage. These techniques can adapt and improvise through continuous learning from new data and evolving attack techniques. This adaptability can improve the system's ability to detect threats and respond effectively to unknown attacks, strengthening security.

Real-time detection and response

Real-time detection over frequent responses is necessary to ensure the security and stability of the proposed systems. ML techniques are generally trained on the previous data and establish the baseline to understand normal behavior, energy consumption, communication patterns, system metrics, and other relevant data. By identifying the anomalies, ML techniques can provide the response mechanisms to isolate the affected nodes, reroute the energy transactions, and activate the alerts and backup systems for further investigation. For example, The proposed system with microgrid application can provide services to hospitals, emergency services, and remote community sectors, requiring real-time detection and responses and ensuring uninterrupted operations and reliability. ML techniques can identify the attacks in time, respond quickly to process the data and ensure the decision is based on learned patterns. This system can ensure security, detect real-time threats, and reduce the vulnerability window that causes minimum damage by utilizing ML techniques.

Evolving security

Evolving threats can be avoided using hybrid consensus and ML algorithms. Consensus algorithms can secure the blockchain, and ML techniques analyze various data sources and detect new attacks, adapting defense systems to ensure security. Data analysis tools can monitor parameters such as network traffic, transaction patterns, user behavior, and other system logs. These systems can analyze new data, improve their ability to detect suspicious activities and perform immediate alerts for further investigation. ML algorithms can perform collaborative acts and rely on information sharing for evolving defense mechanisms. Integrating consensus and ML algorithms can strengthen the synergistic effect and be updated regularly through ongoing research, collaboration, and information sharing within the blockchain community. This system can stay connected to the latest attack techniques, which helps to mitigate emerging threats and ensures long-term security.

Efficient defense mechanism

An effective defense system can be developed using consensus and ML algorithms that detect and respond automatically. In microgrid applications, consensus algorithms can provide authorized transactions that ensure integrity and reliability. ML algorithms can analyze the data, identify patterns, learn from previous attacks, and predict the detection. These ML models will monitor the microgrid system, identify network behavior, and analyze the transaction patterns. This leads to the proposed algorithm detecting potential attacks, identifying the deviations, evolving the triggering process, and mitigating their impacts on microgrid security. Combining hybrid consensus algorithms and ML techniques creates an effective defense mechanism that performs an immediate detection and response to any attacks on the microgrid. Through continuous learning, the ML model develops into an automation process that reduces the administrator's workload, enhances the efficiency of the defense mechanism, and enables real-time detection and response to attacks. These processes can be optimized through iterative learning approaches in ML techniques. Through the application of these methodologies, the proposed system has the potential to enhance the resilience and efficiency of microgrid systems . These algorithms can automate processes and continuously learn, which reduces the workload on system administrators and promotes a proactive approach to security. Ultimately, this helps to defend against attacks while ensuring the uninterrupted operation of the system.

Increased trust in the system

Trust and security are crucial in microgrid systems, and blockchain-based microgrid systems offer a solution that goes beyond. Combining hybrid consensus algorithms and ML approaches can enhance trust in the system's design and improve its adaptability. Using hybrid consensus algorithms and machine learning techniques creates a robust defense system to protect microgrids against potential attacks. This way, it establishes trust by verifying transaction validity and preventing malicious activities through consensus among network participants while also allowing for continuous learning from new data and emerging attack patterns through ML algorithms. Adaptability is of utmost importance in maintaining the effectiveness of defense mechanisms in microgrid systems, especially against constantly evolving and sophisticated strategies. Through ML algorithms, this hybridization can detect anomalies, predict real-time attack patterns, and adjust the defense mechanism accordingly. As a result, this instills increased trust in the microgrid system, enhances its resilience, and reduces its vulnerability to evolving threats.

Open issues and challenges of the hybrid consensus approach

Open issues of the proposed research approach.

Integrating ML, deep learning, and RL with blockchain protocols can improve security, performance, and decision-making capabilities. However, it also presents open issues and challenges that researchers and practitioners must consider carefully. In order to discover the benefits of these technologies in blockchain networks, it is essential to understand and address these challenges. This research analyzes the challenges of integrating intelligent learning algorithms with consensus protocols in blockchain networks. However, it also explores the factors researchers and practitioners should consider to ensure blockchain networks' effective and efficient operation. This analysis identifies critical limitations such as machine-learning algorithms' computational complexity and resource requirements, the need for labeled training data in decentralized and pseudonymous blockchain networks, and adaptive learning approaches in dynamic blockchain networks. Investigations are performed to understand the significance of security and privacy in integrating ML techniques, which includes the vulnerability of models, malicious attacks, and other challenges that ensure interpretability in decision-making processes. However, it is observed that valuable insights into the difficulties of Implementing ML-based consensus algorithms in blockchain. Our findings can also provide future research and development efforts to address the practical concerns and overcome these limitations.

Incorporating hybrid consensus algorithms with ML models will be challenging and complex. To achieve this, we need a strong understanding of consensus protocol, ML algorithms, and sufficient computational resources for training and executing the models. Coordinating the computational resources with consensus protocol and integrating the ML approaches into the hybridization are significant challenges in real-world scenarios.

Data availability and quality

Accurate training of ML models requires diverse and high-quality data. Nevertheless, this can be challenging in blockchain networks, where data privacy and confidentiality are crucial. Furthermore, obtaining labeled data for supervised learning requires specialized expertise and extensive manual work that can be time-consuming and expensive.

Model robustness and generalization

For developing successful hybrid consensus algorithms, the ML model must possess resilience and the capability to perform effective generalization in untested data. Inadequate adaptation to new attack patterns or overfitting can compromise the effectiveness of the models, leading to the identification of false positives or false negatives, which can ultimately weaken the entire system.

Interpretability and explainability

Understanding the reasoning behind ML models can be difficult, as they often function like mysterious black boxes. However, when it comes to consensus protocols, transparency and accountability are crucial, which means that methods must be employed to clarify the choices made by these models. This task can be incredibly daunting when dealing with complex models such as deep learning architectures.

Malicious attacks

It is challenging to be aware of potential threats to the machine learning models, such as false training data, harmful adversarial examples, and weaknesses in the learning process. These attacks can negatively affect the reliability and effectiveness of the hybrid consensus approach. Therefore, it is essential to implement robust defenses to minimize their impact.

Computational overhead

Incorporating ML models into the consensus process could increase the amount of computational work needed. Developing and implementing intricate machine learning models may demand substantial computational resources, which could affect the scalability and effectiveness of the blockchain network.

Ethical considerations

It is imperative to consider ethical implications such as privacy, fairness, and bias when utilizing ML models in consensus protocols. We must take precautions to prevent infringement on user privacy or discriminatory behavior towards certain participants due to biased training data or decision-making processes. Addressing these issues will lead to more effective and equitable use of ML models.

Regulatory and legal considerations

Applying ML models in consensus protocols may raise regulatory and legal challenges, especially in industries with strict data protection and compliance requirements. Compliance with data privacy regulations and ensuring the lawful use of data becomes crucial when integrating ML into consensus mechanisms. Addressing these limitations and challenges requires careful consideration of the specific use case, continuous research and development, and collaboration between domain experts in consensus protocols, machine learning, and cybersecurity. By addressing these challenges, the hybrid consensus approach with ML models can unlock its full potential in enhancing blockchain networks' security, scalability, and adaptability.

Future research directions

The future scope of this research leads to adaptive hybrid models, which focus on developing new consensus mechanisms and ML techniques. These combinations can dynamically adapt to networking situations. It also includes switching between other ML models or consensus mechanisms and responding to the security threats that evolve based on the scenario.

Privacy-preserving ML techniques are essential for advancing research. These hybrid models effectively integrate homomorphic encryption, federated learning, and differential privacy. This ensures that the user's sensitive information remains secure and confidential while performing the learning process.

Future research should focus on self-learning systems, which have the potential to hybrid consensus mechanisms. These systems can adapt autonomously to new threats and optimize the system parameters per the network conditions' real-time feedback. However, energy efficiency is the paramount factor in the blockchain network. Research efforts should focus on developing effective energy consumption-based consensus mechanisms and ML models.

The security and the consensus mechanism's performance can be ensured by establishing security standards and benchmarks for the evaluation process. This facilitates comparing various approaches and contributes to developing new effective models. Cross-collaboration across experts in consensus algorithms, machine learning, cryptography, and cyber security leads to novel methods and insights to overcome real-world problems.

In this research, we have thoroughly analyzed the hybrid consensus algorithm with ML techniques. The challenges and vulnerabilities that exist in a proposed system are concluded using a ProximaX-based decentralized network platform. The findings from this research paper have emphasized the need for effective preventive measures to combat the detrimental impact of cyber-attacks. The proposed research uses hybridized consensus approaches to enhance network security, scalability, and resilience. The proposed ML-based hybrid consensus algorithm mitigates the challenges and vulnerabilities in decentralized public networks. The ML model implemented in this research elevates threat detection, optimizes consensus mechanisms, and ensures the confidentiality of transactions and user data. The decentralized nature of ML-driven security performs proactive attack identification, feature extraction, and anomaly detection, enhancing the consensus protocol's security and reducing cyber-attacks. The proposed model understands the consensus mechanism and retrieves the real-time data and network state, which improves network resilience and decision-making accuracy in the dynamic field of cyber security.

Furthermore, this paper also discusses the implementation challenges of the proposed consensus approaches and their adaptability to a real-world scenario. Further investigation and refinements are required to solve the complexity of the ML model with scalability, resource requirements, computational overhead, and susceptibility to achieve the effectiveness of security and trustworthiness. The future scope of this research leads to the development of an adaptive hybrid model that utilizes novel consensus mechanisms and ML techniques. Privacy-preserving ML techniques and generative learning can autonomously adapt to new threats and optimize the system parameters based on the real-world environment.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Oyinloye, D. P., Sen Teh, J., Jamil, N. & Alawida, M. Blockchain consensus: An overview of alternative protocols. Symmetry 13 (8), 1363 (2021).

Article   ADS   Google Scholar  

Wang, B., Li, Z. & Li, H. Hybrid consensus algorithm based on modified proof-of-probability and DPoS. Futur. Internet 12 (8), 1–16 (2020).

Article   Google Scholar  

Shafay, M. et al. Blockchain for deep learning: Review and open challenges. Cluster Comput. 26 (1), 197–221 (2023).

Article   PubMed   Google Scholar  

Khobragade, P. & Turuk, A. K. Blockchain consensus algorithms: A survey. Lect. Notes Netw. Syst. 595 , 198–210 (2023).

Bachani, V. & Bhattacharjya, A. Preferential delegated proof of stake (PDPoS)—modified DPoS with two layers towards scalability and higher TPS. Symmetry 15 (1), 4 (2023).

Wu, Y., Song, P. & Wang, F. Hybrid consensus algorithm optimization: A mathematical method based on POS and PBFT and its application in blockchain. Math. Probl. Eng. https://doi.org/10.1155/2020/7270624 (2020).

Sakhnini, J., Karimipour, H. & Dehghantanha, A. smart grid cyber attacks detection using supervised learning and heuristic feature selection. in Proceeding of 2019 7th International Conference on Smart Energy Grid Engineering SEGE 2019 , pp. 108–112, (2019).

Mololoth, V. K., Saguna, S. & Åhlund, C. Blockchain and machine learning for future smart grids: A review. Energies 16 (1), 528 (2023).

Ortega-fernandez, I. & Liberati, F. Smart grid using reinforcement learning. 1–15 (2023).

Sahani, N., Zhu, R., Cho, J. H. & Liu, C. C. Machine learning-based intrusion detection for smart grid computing: A survey. ACM Trans. Cyber-Phys. Syst. 7 (2), 1–31 (2023).

Kiayias, A., Russell, A., David, B. & Oliynykov, R. Ouroboros: A provably secure proof-of-stake blockchain protocol , LNCS. 10401, (2017).

Lashkari, B. & Musilek, P. A comprehensive review of blockchain consensus mechanisms. IEEE Access 9 , 43620–43652 (2021).

Xiao, Y., Zhang, N., Lou, W. & Hou, Y. T. A survey of distributed consensus protocols for blockchain networks. IEEE Commun. Surv. Tutorials 22 (2), 1432–1465 (2020).

Andoni, M. et al. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 100 , 143–174 (2019).

Wang, W. et al. A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access 7 , 22328–22370 (2019).

Yang, F. et al. Delegated proof of stake with downgrade: A secure and efficient blockchain consensus algorithm with downgrade mechanism. IEEE Access 7 , 118541–118555 (2019).

Shah, A. A., Malik, H. A. M., Muhammad, A. H., Alourani, A. & Butt, Z. A. Deep learning ensemble 2D CNN approach towards the detection of lung cancer. Sci. Rep. 13 (1), 1–15 (2023).

Wang, X. et al. A long single-span dispersion-decreasing-like fiber transmission system. Opt. Laser Technol. 116 , 338–344 (2019).

Razali, N. F., Isa, I. S., Sulaiman, S. N., Noor, N. K. & Osman, M. K. CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms. Biomed. Signal Process. Control 83 , 104683 (2023).

Yazdinejad, A., Parizi, R. M., Dehghantanha, A. & Choo, K. K. R. P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking. Comput. Secur. 88 , 101629 (2020).

Alam, T., Ullah, A. & Benaida, M. Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. J. Ambient Intell. Humaniz. Comput. 14 (8), 9959–9972 (2023).

Sanwar Hosen, A. S. M. et al. Blockchain-based transaction validation protocol for a secure distributed IoT network. IEEE Access 8 , 117266–117277 (2020).

Michalski, R., Dziubaltowska, D. & MacEk, P. Revealing the character of nodes in a blockchain with supervised learning. IEEE Access 8 , 109639–109647 (2020).

Nasir, M. U., Khan, S., Mehmood, S., Khan, M. A., Zubair, M. & Hwang, S. O. Empowered with blockchain technology. (2022).

Zeadally, S. & Tsikerdekis, M. Securing internet of things (IoT) with machine learning. Int. J. Commun. Syst. 33 (1), 1–16 (2020).

Joshi, K. et al. Machine-learning techniques for predicting phishing attacks in blockchain networks: A comparative study. Algorithms 16 (8), 366 (2023).

Khan, I. A. et al. Fed-inforce-fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. Inf. Fusion 101 , 102002 (2023).

Khan, I. A. et al. A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Appl. Intell. 51 (10), 7306–7321 (2021).

Khan, I. A. et al. Enhancing IIoT networks protection: A robust security model for attack detection in internet industrial control systems. Ad Hoc Netw. 134 , 102930 (2022).

Khan, I. A. et al. An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 23 (12), 25469–25478 (2022).

Sapra, N., Shaikh, I. & Dash, A. Impact of proof of work (PoW)-based blockchain applications on the environment: A systematic review and research agenda. J. Risk Financ. Manag. 16 (4), 218 (2023).

Schinckus, C. Proof-of-work based blockchain technology and anthropocene: An undermined situation?. Renew. Sustain. Energy Rev. 152 , 111682 (2021).

Saad, M., Njilla, L., Kamhoua, C., Kim, J., Nyang, D. & Mohaisen, A. Mempool optimization for defending against DDoS attacks in PoW-based blockchain systems. in ICBC 2019–IEEE International Conference on Blockchain and Cryptocurrency , pp. 285–292 (2019).

Li, W., Cao, M., Wang, Y., Tang, C. & Lin, F. Mining pool game model and nash equilibrium analysis for PoW-based blockchain networks. IEEE Access 8 , 101049–101060 (2020).

Lepore, C. et al. A survey on blockchain consensus with a performance comparison of PoW, PoS and pure PoS. Mathematics 8 (10), 1–26 (2020).

Cao, B. et al. Performance analysis and comparison of PoW, PoS and DAG based blockchains. Digit. Commun. Networks 6 (4), 480–485 (2020).

Liu, D., Alahmadi, A., Ni, J., Lin, X. & Shen, X. Anonymous reputation system for IIoT-enabled retail marketing atop PoS blockchain. IEEE Trans. Ind. Inform. 15 (6), 3527–3537 (2019).

Xu, G., Liu, Y. & Khan, P. W. Improvement of the DPoS consensus mechanism in blockchain based on vague sets. IEEE Trans. Ind. Inform. 16 (6), 4252–4259 (2020).

Zhou, T., Li, X. & Zhao, H. DLattice: A permission-less blockchain based on DPoS-BA-DAG consensus for data tokenization. IEEE Access 7 , 39273–39287 (2019).

Liu, J., Xie, M., Chen, S., Ma, C. & Gong, Q. An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system. Inf. Sci. 575 , 528–541 (2021).

Article   MathSciNet   Google Scholar  

Chen, S., Xie, M., Liu, J. & Zhang, Y. Improvement of the DPoS consensus mechanism in blockchain based on PLTS. in Proceeding of–2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) , pp. 32–37 (2021).

Wang, L., Xu, P., Su, W., Li, Y. & Chen, X. Research on Improvement of blockchain DPOS consensus mechanism based on HK clustering. in Proceeding–2021 China Autom. Congr. CAC 2021 , pp. 1167–1172 (2021).

Li, W. et al. A scalable multi-layer PBFT consensus for blockchain. IEEE Trans. Parallel Distrib. Syst. 32 (5), 1146–1160 (2021).

Chiu, W. Y. & Meng, W. EdgeTC–a PBFT blockchain-based ETC scheme for smart cities. Peer-to-Peer Netw. Appl. 14 (5), 2874–2886 (2021).

Liu, J., Feng, W., Zhang, Y. & He, F. Improvement of PBFT algorithm based on CART. Electronics 12 (6), 1460 (2023).

Liu, S., Zhang, R., Liu, C. & Shi, D. P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability. Comput. Biol. Med. 154 , 106590 (2023).

Wu, Y., Wu, L. & Cai, H. Reinforced practical byzantine fault tolerance consensus protocol for cyber physical systems. Comput. Commun. 203 , 238–247 (2023).

Hu, Y. et al. A practical heartbeat-based defense scheme against cloning Attacks in PoA blockchain. Comput. Stand. Interfaces 83 , 103656 (2023).

Ometov, A. et al. An overview on blockchain for smartphones: state-of-the-art, consensus, implementation, challenges and future trends. IEEE Access 8 , 103994–104015 (2020).

Sasikumar, A. et al. Blockchain-based trust mechanism for digital twin empowered industrial internet of things. Futur. Gener. Comput. Syst. 141 , 16–27 (2023).

Shi, L., Wang, T., Li, J., Zhang, S. & Guo, S. Pooling is not favorable: Decentralize mining power of PoW blockchain using age-of-work. IEEE Trans. Cloud Comput. 11 (3), 2756–2769 (2022).

Google Scholar  

Bandara, E. et al. Casper: A blockchain-based system for efficient and secure customer credential verification. J. Bank. Financ. Technol. 6 (1), 43–62 (2022).

Zhu, S., Cai, Z., Hu, H., Li, Y. & Li, W. zkCrowd: A hybrid blockchain-based crowdsourcing platform. IEEE Trans. Ind. Inform. 16 (6), 4196–4205 (2020).

Buterin, V., Reijsbergen, D., Leonardos, S. & Piliouras, G. Incentives in Ethereum’s hybrid Casper protocol. in ICBC 2019–IEEE International Conference of Blockchain Cryptocurrency , pp. 236–244 (2019).

Sriman, B., Ganesh Kumar, S. & Shamili, P. Blockchain technology: consensus protocol proof of work and proof of stake. Adv. Intell. Syst. Comput. 1172 , 395–406 (2021).

Chen, Y. & Liu, F. Research on improvement of DPoS consensus mechanism in collaborative governance of network public opinion. Peer-to-Peer Netw. Appl. 15 (4), 1849–1861 (2022).

Hasanova, H., Baek, U. J., Shin, M. G., Cho, K. & Kim, M. S. A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. Int. J. Netw. Manag. 29 (2), 1–36 (2019).

Schlatt, V., Guggenberger, T., Schmid, J. & Urbach, N. Attacking the trust machine: Developing an information systems research agenda for blockchain cybersecurity. Int. J. Inf. Manag. 68 , 102470 (2023).

Chaganti, R., Bhushan, B. & Ravi, V. A survey on blockchain solutions in DDoS attacks mitigation: Techniques, open challenges and future directions. Comput. Commun. 197 , 96–112 (2023).

Bhardwaj, A. et al. Penetration testing framework for smart contract Blockchain. Peer-to-Peer Netw. Appl. 14 (5), 2635–2650 (2021).

Liao, K. & Katz, J. Incentivizing Blockchain Forks via Whale Transactions. In Financial cryptography and data security. FC 2017. Lecture notes in computer science Vol. 10323 (eds Brenner, M. et al. ) 264–279 (Springer, 2017).

Iqbal, M. & Matulevicius, R. Exploring sybil and double-spending risks in blockchain systems. IEEE Access 9 , 76153–76177 (2021).

Xu, C. et al. A lightweight and attack-proof bidirectional blockchain paradigm for internet of things. IEEE Internet Things J. 9 (6), 4371–4384 (2022).

Nicolas, K., Wang, Y., Giakos, G. C., Wei, B. & Shen, H. Blockchain system defensive overview for double-spend and selfish mining attacks: A systematic approach. IEEE Access 9 , 3838–3857 (2021).

Li, B. et al. LBS meets blockchain: An efficient method with security preserving trust in SAGIN. IEEE Internet Things J. 9 (8), 5932–5942 (2022).

Nofer, M., Gomber, P., Hinz, O. & Schiereck, D. Blockchain. Bus. Inf. Syst. Eng. 59 (3), 183–187 (2017).

Shin, D. D. H. Blockchain: The emerging technology of digital trust. Telemat. Inform. 45 , 101278 (2019).

Kumar, R. & Sharma, R. Leveraging blockchain for ensuring trust in IoT: A survey. J. King Saud Univ.-Comput. Inf. Sci. 34 (10), 8599–8622 (2022).

Anjum, A., Sporny, M. & Sill, A. Blockchain standards for compliance and trust. IEEE Cloud Comput. 4 (4), 84–90 (2017).

Ismail, L. & Materwala, H. A review of blockchain architecture and consensus protocols: Use cases, challenges, and solutions. Symmetry 11 (10), 1198 (2019).

Khatri, N., Shrestha, R. & Nam, S. Y. Security issues with in-vehicle networks, and enhanced countermeasures based on blockchain. Electronics 10 (8), 1–33 (2021).

Pourrahmani, H., Yavarinasab, A., Monazzah, A. M. H. & Van Herle, J. A review of the security vulnerabilities and countermeasures in the Internet of Things solutions: A bright future for the blockchain. Internet Things 23 , 100888 (2023).

Xia, J., Li, H. & He, Z. The effect of blockchain technology on supply chain collaboration: A case study of Lenovo. Systems 11 (6), 299 (2023).

Connolly, L. Y. & Wall, D. S. The rise of crypto-ransomware in a changing cybercrime landscape: Taxonomising countermeasures. Comput. Secur. 87 , 101568 (2019).

Nguyen, C. T. et al. Proof-of-stake consensus mechanisms for future blockchain networks: Fundamentals, applications and opportunities. IEEE Access 7 , 85727–85745 (2019).

Yusoff, J., Mohamad, Z. & Anuar, M. A review: Consensus algorithms on blockchain. J. Comput. Commun. 10 (09), 37–50 (2022).

Fu, X., Wang, H. & Shi, P. A survey of blockchain consensus algorithms: mechanism, design and applications. Sci. China Inf. Sci. 64 (2), 1–15 (2021).

Bamakan, S. M. H., Motavali, A. & Babaei Bondarti, A. A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl. 154 , 113385 (2020).

Foti, M., Mavromatis, C. & Vavalis, M. Decentralized blockchain-based consensus for optimal power flow solutions. Appl. Energy 283 , 116100 (2021).

Shahsavari, Y., Zhang, K. & Talhi, C. Toward quantifying decentralization of blockchain networks with relay nodes. Front. Blockchain 5 , 1–11 (2022).

Yadav, A. K. et al. A comparative study on consensus mechanism with security threats and future scopes: Blockchain. Comput. Commun. 201 , 102–115 (2023).

Liu, Y., Ke, J., Xu, Q., Jiang, H. & Wang, H. Decentralization is vulnerable under the gap game. IEEE Access 7 , 90999–91008 (2019).

Kim, H., Kim, S. H., Hwang, J. Y. & Seo, C. Efficient privacy-preserving machine learning for blockchain network. IEEE Access 7 , 136481–136495 (2019).

Farooq, M. S. et al. Blockchain-based smart home networks security empowered with fused machine learning. Sensors 22 (12), 1–13 (2022).

Miglani, A. & Kumar, N. Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review. Comput. Commun. 178 , 37–63 (2021).

Ali, J., Khan, R., Ahmad, N. & Maqsood, I. Random forests and decision trees. IJCSI Int. J. Comput. Sci. Issues 9 (5), 272–278 (2012).

Ren, Y. S., Ma, C. Q., Kong, X. L., Baltas, K. & Zureigat, Q. Past, present, and future of the application of machine learning in cryptocurrency research. Res. Int. Bus. Financ. 63 , 101799 (2022).

Ferrag, M. A. & Maglaras, L. DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. 67 (4), 1285–1297 (2020).

Amirzadeh, R., Nazari, A. & Thiruvady, D. Applying artificial intelligence in cryptocurrency markets: A survey. Algorithms 15 (11), 428 (2022).

Keshk, M., Turnbull, B., Moustafa, N., Vatsalan, D. & Choo, K. K. R. A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans. Ind. Inform. 16 (8), 5110–5118 (2020).

Jameel, F. et al. Reinforcement learning in blockchain-enabled IIoT networks: A survey of recent advances and open challenges. Sustainability 12 (12), 1–22 (2020).

Zhang, F., Wang, H., Zhou, L., Xu, D. & Liu, L. A blockchain-based security and trust mechanism for AI-enabled IIoT systems. Futur. Gener. Comput. Syst. 146 , 78–85 (2023).

Dai, Y. et al. Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE Netw. 33 (3), 10–17 (2019).

Qiu, C., Ren, X., Cao, Y. & Mai, T. Deep reinforcement learning empowered adaptivity for future blockchain networks. IEEE Open J. Comput. Soc. 2 , 99–105 (2020).

Mohammed, Z. K. et al. Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets. Ann. Oper. Res. https://doi.org/10.1007/s10479-023-05421-3 (2023).

Jang, H. & Lee, J. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 6 , 5427–5437 (2017).

Raja, L. & Periasamy, P. S. A trusted distributed routing scheme for wireless sensor networks using block chain and jelly fish search optimizer based deep generative adversarial neural network (deep-GANN) technique. Wirel. Pers. Commun. 126 (2), 1101–1128 (2022).

Elsayed, R., Hamada, R., Hammoudeh, M., Abdalla, M. & Elsaid, S. A. A Hierarchical deep learning-based intrusion detection architecture for clustered internet of things. J. Sens. Actuator Netw. 12 (1), 3 (2023).

Dwivedi, A. D., Srivastava, G., Dhar, S. & Singh, R. A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19 (2), 1–17 (2019).

Viswanadham, Y. V. R. S. & Jayavel, K. A framework for data privacy preserving in supply chain management using hybrid meta-heuristic algorithm with ethereum blockchain technology. Electronics 12 (6), 1404 (2023).

Ogundokun, R. O., Misra, S., Maskeliunas, R. & Damasevicius, R. A review on federated learning and machine learning approaches: categorization, application areas, and blockchain technology. Information 13 (5), 263 (2022).

Mekdad, Y. et al. A survey on security and privacy issues of UAVs. Comput. Networks 224 , 362–367 (2023).

Frimpong, S. A. et al. RecGuard: An efficient privacy preservation blockchain-based system for online social network users. Blockchain Res. Appl. 4 (1), 100111 (2023).

Qi, J. & Guan, Y. Practical Byzantine fault tolerance consensus based on comprehensive reputation. Peer-to-Peer Netw. Appl. 16 (1), 420–430 (2023).

Sajana, P., Sindhu, M. & Sethumadhavan, M. On blockchain applications: Hyperledger fabric and ethereum. Int. J. Pure Appl. Math. 118 (18), 2965–2969 (2018).

Nakandala, S. et al . A tensor compiler for unified machine learning prediction serving. in Proceeding of 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020 , pp. 899–917 (2020).

Alsheikh, M. A., Lin, S., Niyato, D. & Tan, H. P. Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Commun. Surv. Tutorials 16 (4), 1996–2018 (2014).

Mao, Q., Hu, F. & Hao, Q. Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Commun. Surv. Tutorials 20 (4), 2595–2621 (2018).

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Acknowledgements

“This research work was supported and funded by the National Defence University Malaysia (NDUM)—UPNM/2023/GPPP/ICT/1 and UPNM/2022/GPJP/ICT/3”.

This research received funding from the corresponding research grants: UPNM/2023/GPPP/ICT/1 and UPNM/2022/GPJP/ICT/3.

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This book is an attempt to aim at a very futuristic vision of achieving self-powered cyber-physical systems by applying a multitude of current technologies such as ULP electronics, thin film electronics, ULP transducers, autonomous wireless sensor networks using energy harvesters at the component level and energy efficient clean energy for powering data centers and machines at the system level. This is the need of the hour for cyber-physical systems since data requires energy when it is stored, transmitted, or converted to other forms. Cyber-physical systems will become energy hungry since the industry trend is towards ubiquitous computing with massive deployment of sensors and actuators. This is evident in using blockchain technologies such as Bitcoin or running epochs for artificial intelligence (AI) applications. Hence, there is a need for research to understand energy patterns and distribution in cyber-physical systems and adopt new technologies to transcend to self-powered cyber-physical systems. This book explores the recent trends in energy management, self-powered devices, and methods in the cyber-physical world.

Written and edited by a team of experts in the field, this book tackles a multitude of subjects related to cyber physical systems (CPSs), including self-powered sensory transducers, ambient energy harvesting for wireless sensor networks, actuator methods and non-contact sensing equipment for soft robots, alternative optimization strategies for DGDCs to improve task distribution and provider profits, wireless power transfer methods, machine learning algorithms for CPS and IoT applications, integration of renewables, electric vehicles (EVs), smart grids, RES micro-grid and EV systems for effective load matching, self-powered car cyber-physical systems, anonymous routing and intrusion detection systems for VANET security, data-driven pavement distress prediction methods, the impact of autonomous vehicles on industries and the auto insurance market, Intelligent transportation systems and associated security concerns, digital twin prototypes and their automotive applications, farming robotics for CPS farming, self-powered CPS in smart cities, self-powered CPS in healthcare and biomedical devices, cyber-security considerations, societal impact and ethical concerns, and advances in human-machine interfaces and explore the integration of self-powered CPS in industrial automation. Whether for the veteran engineer or student, this volume is a must-have for any library.

Rathishchandra R Gatti, PhD, is a professor and head of the Department of Mechanical Engineering and Robotics and Automation at the Sahyadri College of Engineering and Management, Mangalore, India. He holds four patents, has published over 40 peer-reviewed publications and is the editor of seven books and one journal. He has over 20 years of R&D experience in mechanical engineering and mechatronics.

Chandra Singh, MTech, is an assistant professor in the Department of Electronics and Communication Engineering at the Sahyadri College of Engineering and Management, Mangalore, India and is pursuing his PhD. He holds four patents, has written over 25 peer-reviewed publications and is the editor of 7 books.

Rajeev Agrawal, PhD, is an associate professor in the Department of Mechanical Engineering, at the Malaviya National Institute of Technology, Jaipur, India. He has more than 24 years of professional experience and serves on the editorial board of three international journals and has guest-edited over 10 journals and books. He has published over 100 research papers.

Felcy Jyothi Serrao, PhD, is an associate professor in the Department of Physics at Sahyadri College of Engineering and Management, India. She has 19 years of teaching experience and 12 years of research experience in the field of semiconducting metal oxide nanofilms. She has published more than 18 research articles in peer-reviewed journals and conferences.

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A confidential batch payment scheme with integrated auditing for enhanced data trading security.

1. Introduction

• We propose a confidential batch payment scheme with integrated auditing for enhanced data trading security. Our scheme ensures payment privacy, allows senders to compile payment statistic analysis, and enables independent auditing by two separate auditors.
• We propose a framework for the batch payment scheme, providing a scheme instance that meets CCA security under a game-based security model.
• We conduct a comparative analysis which indicates that our scheme distinctively balances privacy and audit needs in blockchain payments, emphasizing efficiency and privacy. Test results demonstrate practical encryption, decryption, and audit time of 0.89/1.55/1.55 ms, respectively.

2. Related Works

3. system framework, 4. scheme construction, 5. security analysis.

• B simulates four random oracles H 1 , H 2 , H 3 , H 4 .
• B chooses random z 1 , z 2 ∈ Z q and computes its public key as: P K = ( h , t ) = ( g q a , g q z 1 + z 2 · a ) ,
• B sends P K to A .
• Check for existing query: If the query exists in the hash list, B returns the previously recorded response.
• Generate new response: Otherwise, B selects random values s i , j and t i , j , computes s i , j = H 1 ( r i , j , h b i , j , t b i , j ) , and sends s i , j , t i , j to A , and adds ( r i , j , h b i , j , t b i , j , s i , j ) to the hash list.

6. Comparison

• The ciphertext length of [ 17 ] is ( 32 n + 64 n + 608 n ) bytes.
• The ciphertext length of [ 26 ] is ( 64 n + 96 n + 608 n ) bytes.
• The ciphertext length of [ 23 ] is 256 n + ( 2563 + 32 ) n + 3424 n bytes.
• The ciphertext length of our scheme is 64 n + ( 64 n + 32 ) + ( 2 log 2 ( n ) + 14 ) 32 bytes.

7. Experimental Analysis

8. conclusions, author contributions, data availability statement, conflicts of interest.

• He, Y.; Zhu, H.; Wang, C.; Xiao, K.; Zhou, Y.; Xin, Y. An accountable data trading platform based on blockchain. In Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France, 29 April–2 May 2019; pp. 1–6. [ Google Scholar ]
• Li, C.; Liang, S.; Zhang, J.; Wang, Q.E.; Luo, Y. Blockchain-based data trading in edge-cloud computing environment. Inf. Process. Manag. 2022 , 59 , 102786. [ Google Scholar ] [ CrossRef ]
• Delgado-Segura, S.; Pérez-Solà, C.; Navarro-Arribas, G.; Herrera-Joancomartí, J. A fair protocol for data trading based on bitcoin transactions. Future Gener. Comput. Syst. 2020 , 107 , 832–840. [ Google Scholar ] [ CrossRef ]
• Yazdinejad, A.; Dehghantanha, A.; Karimipour, H.; Srivastava, G.; Parizi, R.M. A robust privacy-preserving federated learning model against model poisoning attacks. IEEE Trans. Inf. Forensics Secur. 2024 , 19 , 6693–6708. [ Google Scholar ] [ CrossRef ]
• Yazdinejad, A.; Dehghantanha, A.; Srivastava, G. AP2FL: Auditable privacy-preserving federated learning framework for electronics in healthcare. IEEE Trans. Consum. Electron. 2023 , 70 , 2527–2535. [ Google Scholar ] [ CrossRef ]
• Yazdinejad, A.; Dehghantanha, A.; Srivastava, G.; Karimipour, H.; Parizi, R.M. Hybrid privacy preserving federated learning against irregular users in next-generation Internet of Things. J. Syst. Archit. 2024 , 148 , 103088. [ Google Scholar ] [ CrossRef ]
• Li, T.; Yang, A.; Weng, J.; Tong, Y.; Pei, Q. Concurrent and efficient IoT data trading based on probabilistic micropayments. Wirel. Netw. 2023 , 29 , 607–622. [ Google Scholar ] [ CrossRef ]
• Zheng, S.; Pan, L.; Hu, D.; Li, M.; Fan, Y. A blockchain-based trading platform for big data. In Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Virtual, 6–9 July 2020; pp. 991–996. [ Google Scholar ]
• Li, Z.; Kang, J.; Yu, R.; Ye, D.; Deng, Q.; Zhang, Y. Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans. Ind. Inform. 2017 , 14 , 3690–3700. [ Google Scholar ] [ CrossRef ]
• Di Francesco, M.; Marchesi, L.; Porcu, R. Kryptosafe: Managing and trading data sets using blockchain and IPFS. In Proceedings of the 2023 IEEE/ACM 6th International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), Melbourne, Australia, 14–20 May 2023; pp. 5–8. [ Google Scholar ]
• Zhao, Y.; Yu, Y.; Li, Y.; Han, G.; Du, X. Machine learning based privacy-preserving fair data trading in big data market. Inf. Sci. 2019 , 478 , 449–460. [ Google Scholar ] [ CrossRef ]
• Li, B.; He, Q.; Chen, F.; Jin, H.; Xiang, Y.; Yang, Y. Auditing cache data integrity in the edge computing environment. IEEE Trans. Parallel Distrib. Syst. 2020 , 32 , 1210–1223. [ Google Scholar ] [ CrossRef ]
• Dai, W.; Dai, C.; Choo, K.K.R.; Cui, C.; Zou, D.; Jin, H. SDTE: A secure blockchain-based data trading ecosystem. IEEE Trans. Inf. Forensics Secur. 2019 , 15 , 725–737. [ Google Scholar ] [ CrossRef ]
• Li, Y.N.; Feng, X.; Xie, J.; Feng, H.; Guan, Z.; Wu, Q. A decentralized and secure blockchain platform for open fair data trading. Concurr. Comput. Pract. Exp. 2020 , 32 , e5578. [ Google Scholar ] [ CrossRef ]
• Guan, Z.; Shao, X.; Wan, Z. Secure fair and efficient data trading without third party using blockchain. In Proceedings of the 2018 IEEE International Conference on Internet of Things, Halifax, NS, Canada, 30 July–3 August 2018; pp. 1395–1401. [ Google Scholar ]
• An, B.; Xiao, M.; Liu, A.; Xu, Y.; Zhang, X.; Li, Q. Secure crowdsensed data trading based on blockchain. IEEE Trans. Mob. Comput. 2021 , 22 , 1763–1778. [ Google Scholar ] [ CrossRef ]
• Maxwell, G. Confidential Transactions. Available online: https://www.weusecoins.com/confidential-transactions/ (accessed on 16 June 2015).
• Miers, I.; Garman, C.; Green, M.; Rubin, A.D. Zerocoin: Anonymous Distributed E-Cash from Bitcoin. In Proceedings of the 2013 IEEE Symposium on Security and Privacy, San Francisco, CA, USA, 19–22 May 2013; pp. 397–411. [ Google Scholar ]
• Ben Sasson, E.; Chiesa, A.; Garman, C.; Green, M.; Miers, I.; Tromer, E.; Virza, M. Zerocash: Decentralized Anonymous Payments from Bitcoin. In Proceedings of the 2014 IEEE Symposium on Security and Privacy, San Jose, CA, USA, 18–21 May 2014; pp. 459–474. [ Google Scholar ]
• Chen, T.; Lu, H.; Kunpittaya, T.; Luo, A. A Review of zk-SNARKs. arXiv 2023 , arXiv:2202.06877. [ Google Scholar ]
• Sun, S.F.; Au, M.H.; Liu, J.K.; Yuen, T.H. Ringct 2.0: A compact accumulator-based (linkable ring signature) protocol for blockchain cryptocurrency monero. In Proceedings of the Computer Security–ESORICS 2017: 22nd European Symposium on Research in Computer Security, Oslo, Norway, 11–15 September 2017; pp. 456–474. [ Google Scholar ]
• Boneh, D.; Bünz, B.; Fisch, B. Batching techniques for accumulators with applications to IOPs and stateless blockchains. In Proceedings of the Advances in Cryptology–CRYPTO 2019: 39th Annual International Cryptology Conference, Santa Barbara, CA, USA, 18–22 August 2019; pp. 561–586. [ Google Scholar ]
• Wang, Q.; Qin, B.; Hu, J.; Xiao, F. Preserving transaction privacy in bitcoin. Future Gener. Comput. Syst. 2020 , 107 , 793–804. [ Google Scholar ] [ CrossRef ]
• Castagnos, G.; Catalano, D.; Laguillaumie, F.; Savasta, F.; Tucker, I. Bandwidth-efficient threshold EC-DSA revisited: Online/offline extensions, identifiable aborts proactive and adaptive security. Theor. Comput. Sci. 2023 , 939 , 78–104. [ Google Scholar ] [ CrossRef ]
• Guo, F.; Susilo, W.; Mu, Y. Introduction to Security Reduction ; Springer: Cham, Switzerland, 2018. [ Google Scholar ]
• Domadiya, N.; Rao, U.P. ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data. J. Inst. Eng. (India) Ser. B 2022 , 103 , 817–830. [ Google Scholar ] [ CrossRef ]
• Wang, H.; Qin, H.; Zhao, M.; Wei, X.; Shen, H.; Susilo, W. Blockchain-based fair payment smart contract for public cloud storage auditing. Inf. Sci. 2020 , 519 , 348–362. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Wang, Z.; Zhong, L.; Zhao, L.; Wang, Y.; Zhu, Z. A Confidential Batch Payment Scheme with Integrated Auditing for Enhanced Data Trading Security. Electronics 2024 , 13 , 3278. https://doi.org/10.3390/electronics13163278

Wang Z, Zhong L, Zhao L, Wang Y, Zhu Z. A Confidential Batch Payment Scheme with Integrated Auditing for Enhanced Data Trading Security. Electronics . 2024; 13(16):3278. https://doi.org/10.3390/electronics13163278

Wang, Zheng, Lin Zhong, Liutao Zhao, Yujue Wang, and Zhongshan Zhu. 2024. "A Confidential Batch Payment Scheme with Integrated Auditing for Enhanced Data Trading Security" Electronics 13, no. 16: 3278. https://doi.org/10.3390/electronics13163278

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