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Reference List: Common Reference List Examples

Article (with doi).

Alvarez, E., & Tippins, S. (2019). Socialization agents that Puerto Rican college students use to make financial decisions. Journal of Social Change , 11 (1), 75–85. https://doi.org/10.5590/JOSC.2019.11.1.07

Laplante, J. P., & Nolin, C. (2014). Consultas and socially responsible investing in Guatemala: A case study examining Maya perspectives on the Indigenous right to free, prior, and informed consent. Society & Natural Resources , 27 , 231–248. https://doi.org/10.1080/08941920.2013.861554

Use the DOI number for the source whenever one is available. DOI stands for "digital object identifier," a number specific to the article that can help others locate the source. In APA 7, format the DOI as a web address. Active hyperlinks for DOIs and URLs should be used for documents meant for screen reading. Present these hyperlinks in blue and underlined text (the default formatting in Microsoft Word), although plain black text is also acceptable. Be consistent in your formatting choice for DOIs and URLs throughout your reference list. Also see our Quick Answer FAQ, "Can I use the DOI format provided by library databases?"

Jerrentrup, A., Mueller, T., Glowalla, U., Herder, M., Henrichs, N., Neubauer, A., & Schaefer, J. R. (2018). Teaching medicine with the help of “Dr. House.” PLoS ONE , 13 (3), Article e0193972. https://doi.org/10.1371/journal.pone.0193972

For journal articles that are assigned article numbers rather than page ranges, include the article number in place of the page range.

For more on citing electronic resources, see  Electronic Sources References .

Article (Without DOI)

Found in a common academic research database or in print.

Casler , T. (2020). Improving the graduate nursing experience through support on a social media platform. MEDSURG Nursing , 29 (2), 83–87.

If an article does not have a DOI and you retrieved it from a common academic research database through the university library, there is no need to include any additional electronic retrieval information. The reference list entry looks like the entry for a print copy of the article. (This format differs from APA 6 guidelines that recommended including the URL of a journal's homepage when the DOI was not available.) Note that APA 7 has additional guidance on reference list entries for articles found only in specific databases or archives such as Cochrane Database of Systematic Reviews, UpToDate, ProQuest Dissertations and Theses Global, and university archives. See APA 7, Section 9.30 for more information.

Found on an Open Access Website

Eaton, T. V., & Akers, M. D. (2007). Whistleblowing and good governance. CPA Journal , 77 (6), 66–71. http://archives.cpajournal.com/2007/607/essentials/p58.htm

Provide the direct web address/URL to a journal article found on the open web, often on an open access journal's website. In APA 7, active hyperlinks for DOIs and URLs should be used for documents meant for screen reading. Present these hyperlinks in blue and underlined text (the default formatting in Microsoft Word), although plain black text is also acceptable. Be consistent in your formatting choice for DOIs and URLs throughout your reference list.

Weinstein, J. A. (2010).  Social change  (3rd ed.). Rowman & Littlefield.

If the book has an edition number, include it in parentheses after the title of the book. If the book does not list any edition information, do not include an edition number. The edition number is not italicized.

American Nurses Association. (2015). Nursing: Scope and standards of practice (3rd ed.).

If the author and publisher are the same, only include the author in its regular place and omit the publisher.

Lencioni, P. (2012). The advantage: Why organizational health trumps everything else in business . Jossey-Bass. https://amzn.to/343XPSJ

As a change from APA 6 to APA 7, it is no longer necessary to include the ebook format in the title. However, if you listened to an audiobook and the content differs from the text version (e.g., abridged content) or your discussion highlights elements of the audiobook (e.g., narrator's performance), then note that it is an audiobook in the title element in brackets. For ebooks and online audiobooks, also include the DOI number (if available) or nondatabase URL but leave out the electronic retrieval element if the ebook was found in a common academic research database, as with journal articles. APA 7 allows for the shortening of long DOIs and URLs, as shown in this example. See APA 7, Section 9.36 for more information.

Chapter in an Edited Book

Poe, M. (2017). Reframing race in teaching writing across the curriculum. In F. Condon & V. A. Young (Eds.), Performing antiracist pedagogy in rhetoric, writing, and communication (pp. 87–105). University Press of Colorado.

Include the page numbers of the chapter in parentheses after the book title.

Christensen, L. (2001). For my people: Celebrating community through poetry. In B. Bigelow, B. Harvey, S. Karp, & L. Miller (Eds.), Rethinking our classrooms: Teaching for equity and justice (Vol. 2, pp. 16–17). Rethinking Schools.

Also include the volume number or edition number in the parenthetical information after the book title when relevant.

Freud, S. (1961). The ego and the id. In J. Strachey (Ed.),  The standard edition of the complete psychological works of Sigmund Freud  (Vol. 19, pp. 3-66). Hogarth Press. (Original work published 1923)

When a text has been republished as part of an anthology collection, after the author’s name include the date of the version that was read. At the end of the entry, place the date of the original publication inside parenthesis along with the note “original work published.” For in-text citations of republished work, use both dates in the parenthetical citation, original date first with a slash separating the years, as in this example: Freud (1923/1961). For more information on reprinted or republished works, see APA 7, Sections 9.40-9.41.

Classroom Resources

Citing classroom resources.

If you need to cite content found in your online classroom, use the author (if there is one listed), the year of publication (if available), the title of the document, and the main URL of Walden classrooms. For example, you are citing study notes titled "Health Effects of Exposure to Forest Fires," but you do not know the author's name, your reference entry will look like this:

Health effects of exposure to forest fires [Lecture notes]. (2005). Walden University Canvas. https://waldenu.instructure.com

If you do know the author of the document, your reference will look like this:

Smith, A. (2005). Health effects of exposure to forest fires [PowerPoint slides]. Walden University Canvas. https://waldenu.instructure.com  

A few notes on citing course materials:

• [Lecture notes]
• [Course handout]
• [Study notes]
• It can be difficult to determine authorship of classroom documents. If an author is listed on the document, use that. If the resource is clearly a product of Walden (such as the course-based videos), use Walden University as the author. If you are unsure or if no author is indicated, place the title in the author spot, as above.
• If you cannot determine a date of publication, you can use n.d. (for "no date") in place of the year.

Note:  The web location for Walden course materials is not directly retrievable without a password, and therefore, following APA guidelines, use the main URL for the class sites: https://class.waldenu.edu.

Citing Tempo Classroom Resources

Clear author: 

Smith, A. (2005). Health effects of exposure to forest fires [PowerPoint slides]. Walden University Brightspace. https://mytempo.waldenu.edu

Unclear author:

Health effects of exposure to forest fires [Lecture notes]. (2005). Walden University Brightspace. https://mytempo.waldenu.edu

Conference Sessions and Presentations

Feinman, Y. (2018, July 27). Alternative to proctoring in introductory statistics community college courses [Poster presentation]. Walden University Research Symposium, Minneapolis, MN, United States. https://scholarworks.waldenu.edu/symposium2018/23/

Torgerson, K., Parrill, J., & Haas, A. (2019, April 5-9). Tutoring strategies for online students [Conference session]. The Higher Learning Commission Annual Conference, Chicago, IL, United States. http://onlinewritingcenters.org/scholarship/torgerson-parrill-haas-2019/

Dictionary Entry

Merriam-Webster. (n.d.). Leadership. In Merriam-Webster.com dictionary . Retrieved May 28, 2020, from https://www.merriam-webster.com/dictionary/leadership

When constructing a reference for an entry in a dictionary or other reference work that has no byline (i.e., no named individual authors), use the name of the group—the institution, company, or organization—as author (e.g., Merriam Webster, American Psychological Association, etc.). The name of the entry goes in the title position, followed by "In" and the italicized name of the reference work (e.g., Merriam-Webster.com dictionary , APA dictionary of psychology ). In this instance, APA 7 recommends including a retrieval date as well for this online source since the contents of the page change over time. End the reference entry with the specific URL for the defined word.

Discussion Board Post

Osborne, C. S. (2010, June 29). Re: Environmental responsibility [Discussion post]. Walden University Canvas.  https://waldenu.instructure.com  

Dissertations or Theses

Retrieved From a Database

Nalumango, K. (2019). Perceptions about the asylum-seeking process in the United States after 9/11 (Publication No. 13879844) [Doctoral dissertation, Walden University]. ProQuest Dissertations and Theses.

Retrieved From an Institutional or Personal Website

Evener. J. (2018). Organizational learning in libraries at for-profit colleges and universities [Doctoral dissertation, Walden University]. ScholarWorks. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=6606&context=dissertations

Unpublished Dissertation or Thesis

Kirwan, J. G. (2005). An experimental study of the effects of small-group, face-to-face facilitated dialogues on the development of self-actualization levels: A movement towards fully functional persons [Unpublished doctoral dissertation]. Saybrook Graduate School and Research Center.

For further examples and information, see APA 7, Section 10.6.

Legal Material

For legal references, APA follows the recommendations of The Bluebook: A Uniform System of Citation , so if you have any questions beyond the examples provided in APA, seek out that resource as well.

Court Decisions

Reference format:

Name v. Name, Volume Reporter Page (Court Date). URL

Sample reference entry:

Brown v. Board of Education, 347 U.S. 483 (1954). https://www.oyez.org/cases/1940-1955/347us483

Sample citation:

In Brown v. Board of Education (1954), the Supreme Court ruled racial segregation in schools unconstitutional.

Note: Italicize the case name when it appears in the text of your paper.

Name of Act, Title Source § Section Number (Year). URL

Sample reference entry for a federal statute:

Individuals With Disabilities Education Act, 20 U.S.C. § 1400 et seq. (2004). https://www.congress.gov/108/plaws/publ446/PLAW-108publ446.pdf

Sample reference entry for a state statute:

Minnesota Nurse Practice Act, Minn. Stat. §§ 148.171 et seq. (2019). https://www.revisor.mn.gov/statutes/cite/148.171

Sample citation: Minnesota nurses must maintain current registration in order to practice (Minnesota Nurse Practice Act, 2010).

Note: The § symbol stands for "section." Use §§ for sections (plural). To find this symbol in Microsoft Word, go to "Insert" and click on Symbol." Look in the Latin 1-Supplement subset. Note: U.S.C. stands for "United States Code." Note: The Latin abbreviation " et seq. " means "and what follows" and is used when the act includes the cited section and ones that follow. Note: List the chapter first followed by the section or range of sections.

Unenacted Bills and Resolutions

(Those that did not pass and become law)

Title [if there is one], bill or resolution number, xxx Cong. (year). URL

Sample reference entry for Senate bill:

Anti-Phishing Act, S. 472, 109th Cong. (2005). https://www.congress.gov/bill/109th-congress/senate-bill/472

Sample reference entry for House of Representatives resolution:

Anti-Phishing Act, H.R. 1099, 109th Cong. (2005). https://www.congress.gov/bill/109th-congress/house-bill/1099

The Anti-Phishing Act (2005) proposed up to 5 years prison time for people running Internet scams.

These are the three legal areas you may be most apt to cite in your scholarly work. For more examples and explanation, see APA 7, Chapter 11.

Magazine Article

Clay, R. (2008, June). Science vs. ideology: Psychologists fight back about the misuse of research. Monitor on Psychology , 39 (6). https://www.apa.org/monitor/2008/06/ideology

Note that for citations, include only the year: Clay (2008). For magazine articles retrieved from a common academic research database, leave out the URL. For magazine articles from an online news website that is not an online version of a print magazine, follow the format for a webpage reference list entry.

Newspaper Article (Retrieved Online)

Baker, A. (2014, May 7). Connecticut students show gains in national tests. New York Times . http://www.nytimes.com/2014/05/08/nyregion/national-assessment-of-educational-progress-results-in-Connecticut-and-New-Jersey.html

Include the full date in the format Year, Month Day. Do not include a retrieval date for periodical sources found on websites. Note that for citations, include only the year: Baker (2014). For newspaper articles retrieved from a common academic research database, leave out the URL. For newspaper articles from an online news website that is not an online version of a print newspaper, follow the format for a webpage reference list entry.

OASIS Resources

Oasis webpage.

OASIS. (n.d.). Common reference list examples . Walden University. https://academicguides.waldenu.edu/writingcenter/apa/references/examples

For all OASIS content, list OASIS as the author. Because OASIS webpages do not include publication dates, use “n.d.” for the year.

Interactive Guide

OASIS. (n.d.). Embrace iterative research and writing [Interactive guide]. Walden University. https://academics.waldenu.edu/oasis/iterative-research-writing-web

For OASIS multimedia resources, such as interactive guides, include a description of the resource in brackets after the title.

Online Video/Webcast

Walden University. (2013).  An overview of learning  [Video]. Walden University Canvas.  https://waldenu.instructure.com  

Use this format for online videos such as Walden videos in classrooms. Most of our classroom videos are produced by Walden University, which will be listed as the author in your reference and citation. Note: Some examples of audiovisual materials in the APA manual show the word “Producer” in parentheses after the producer/author area. In consultation with the editors of the APA manual, we have determined that parenthetical is not necessary for the videos in our courses. The manual itself is unclear on the matter, however, so either approach should be accepted. Note that the speaker in the video does not appear in the reference list entry, but you may want to mention that person in your text. For instance, if you are viewing a video where Tobias Ball is the speaker, you might write the following: Tobias Ball stated that APA guidelines ensure a consistent presentation of information in student papers (Walden University, 2013). For more information on citing the speaker in a video, see our page on Common Citation Errors .

Taylor, R. [taylorphd07]. (2014, February 27). Scales of measurement [Video]. YouTube. https://www.youtube.com/watch?v=PDsMUlexaMY

OASIS. (2020, April 15). One-way ANCOVA: Introduction [Video]. YouTube. https://youtu.be/_XnNDQ5CNW8

For videos from streaming sites, use the person or organization who uploaded the video in the author space to ensure retrievability, whether or not that person is the speaker in the video. A username can be provided in square brackets. As a change from APA 6 to APA 7, include the publisher after the title, and do not use "Retrieved from" before the URL. See APA 7, Section 10.12 for more information and examples.

See also reference list entry formats for TED Talks .

Technical and Research Reports

Edwards, C. (2015). Lighting levels for isolated intersections: Leading to safety improvements (Report No. MnDOT 2015-05). Center for Transportation Studies. http://www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=2402

Technical and research reports by governmental agencies and other research institutions usually follow a different publication process than scholarly, peer-reviewed journals. However, they present original research and are often useful for research papers. Sometimes, researchers refer to these types of reports as gray literature , and white papers are a type of this literature. See APA 7, Section 10.4 for more information.

Reference list entires for TED Talks follow the usual guidelines for multimedia content found online. There are two common places to find TED talks online, with slightly different reference list entry formats for each.

TED Talk on the TED website

If you find the TED Talk on the TED website, follow the format for an online video on an organizational website:

Owusu-Kesse, K. (2020, June). 5 needs that any COVID-19 response should meet [Video]. TED Conferences. https://www.ted.com/talks/kwame_owusu_kesse_5_needs_that_any_covid_19_response_should_meet

The speaker is the author in the reference list entry if the video is posted on the TED website. For citations, use the speaker's surname.

TED Talk on YouTube

If you find the TED Talk on YouTube or another streaming video website, follow the usual format for streaming video sites:

TED. (2021, February 5). The shadow pandemic of domestic violence during COVID-19 | Kemi DaSilvalbru [Video]. YouTube. https://www.youtube.com/watch?v=PGdID_ICFII

TED is the author in the reference list entry if the video is posted on YouTube since it is the channel on which the video is posted. For citations, use TED as the author.

Walden University Course Catalog

To include the Walden course catalog in your reference list, use this format:

Walden University. (2020). 2019-2020 Walden University catalog . https://catalog.waldenu.edu/index.php

If you cite from a specific portion of the catalog in your paper, indicate the appropriate section and paragraph number in your text:

...which reflects the commitment to social change expressed in Walden University's mission statement (Walden University, 2020, Vision, Mission, and Goals section, para. 2).

And in the reference list:

Walden University. (2020). Vision, mission, and goals. In 2019-2020 Walden University catalog. https://catalog.waldenu.edu/content.php?catoid=172&navoid=59420&hl=vision&returnto=search

Vartan, S. (2018, January 30). Why vacations matter for your health . CNN. https://www.cnn.com/travel/article/why-vacations-matter/index.html

For webpages on the open web, include the author, date, webpage title, organization/site name, and URL. (There is a slight variation for online versions of print newspapers or magazines. For those sources, follow the models in the previous sections of this page.)

American Federation of Teachers. (n.d.). Community schools . http://www.aft.org/issues/schoolreform/commschools/index.cfm

If there is no specified author, then use the organization’s name as the author. In such a case, there is no need to repeat the organization's name after the title.

In APA 7, active hyperlinks for DOIs and URLs should be used for documents meant for screen reading. Present these hyperlinks in blue and underlined text (the default formatting in Microsoft Word), although plain black text is also acceptable. Be consistent in your formatting choice for DOIs and URLs throughout your reference list.

Related Resources

Knowledge Check: Common Reference List Examples

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• Knowledge Base
• Referencing

A Quick Guide to Referencing | Cite Your Sources Correctly

Referencing means acknowledging the sources you have used in your writing. Including references helps you support your claims and ensures that you avoid plagiarism .

There are many referencing styles, but they usually consist of two things:

• A citation wherever you refer to a source in your text.
• A reference list or bibliography at the end listing full details of all your sources.

The most common method of referencing in UK universities is Harvard style , which uses author-date citations in the text. Our free Harvard Reference Generator automatically creates accurate references in this style.

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Table of contents

Referencing styles, citing your sources with in-text citations, creating your reference list or bibliography, harvard referencing examples, frequently asked questions about referencing.

Each referencing style has different rules for presenting source information. For in-text citations, some use footnotes or endnotes , while others include the author’s surname and date of publication in brackets in the text.

The reference list or bibliography is presented differently in each style, with different rules for things like capitalisation, italics, and quotation marks in references.

Your university will usually tell you which referencing style to use; they may even have their own unique style. Always follow your university’s guidelines, and ask your tutor if you are unsure. The most common styles are summarised below.

Harvard referencing, the most commonly used style at UK universities, uses author–date in-text citations corresponding to an alphabetical bibliography or reference list at the end.

Harvard Referencing Guide

Vancouver referencing, used in biomedicine and other sciences, uses reference numbers in the text corresponding to a numbered reference list at the end.

Vancouver Referencing Guide

APA referencing, used in the social and behavioural sciences, uses author–date in-text citations corresponding to an alphabetical reference list at the end.

APA Referencing Guide APA Reference Generator

MHRA referencing, used in the humanities, uses footnotes in the text with source information, in addition to an alphabetised bibliography at the end.

MHRA Referencing Guide

OSCOLA referencing, used in law, uses footnotes in the text with source information, and an alphabetical bibliography at the end in longer texts.

OSCOLA Referencing Guide

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In-text citations should be used whenever you quote, paraphrase, or refer to information from a source (e.g. a book, article, image, website, or video).

Quoting and paraphrasing

Quoting is when you directly copy some text from a source and enclose it in quotation marks to indicate that it is not your own writing.

Paraphrasing is when you rephrase the original source into your own words. In this case, you don’t use quotation marks, but you still need to include a citation.

In most referencing styles, page numbers are included when you’re quoting or paraphrasing a particular passage. If you are referring to the text as a whole, no page number is needed.

In-text citations

In-text citations are quick references to your sources. In Harvard referencing, you use the author’s surname and the date of publication in brackets.

Up to three authors are included in a Harvard in-text citation. If the source has more than three authors, include the first author followed by ‘ et al. ‘

The point of these citations is to direct your reader to the alphabetised reference list, where you give full information about each source. For example, to find the source cited above, the reader would look under ‘J’ in your reference list to find the title and publication details of the source.

Placement of in-text citations

In-text citations should be placed directly after the quotation or information they refer to, usually before a comma or full stop. If a sentence is supported by multiple sources, you can combine them in one set of brackets, separated by a semicolon.

If you mention the author’s name in the text already, you don’t include it in the citation, and you can place the citation immediately after the name.

• Another researcher warns that the results of this method are ‘inconsistent’ (Singh, 2018, p. 13) .
• Previous research has frequently illustrated the pitfalls of this method (Singh, 2018; Jones, 2016) .
• Singh (2018, p. 13) warns that the results of this method are ‘inconsistent’.

The terms ‘bibliography’ and ‘reference list’ are sometimes used interchangeably. Both refer to a list that contains full information on all the sources cited in your text. Sometimes ‘bibliography’ is used to mean a more extensive list, also containing sources that you consulted but did not cite in the text.

A reference list or bibliography is usually mandatory, since in-text citations typically don’t provide full source information. For styles that already include full source information in footnotes (e.g. OSCOLA and Chicago Style ), the bibliography is optional, although your university may still require you to include one.

Format of the reference list

Reference lists are usually alphabetised by authors’ last names. Each entry in the list appears on a new line, and a hanging indent is applied if an entry extends onto multiple lines.

Different source information is included for different source types. Each style provides detailed guidelines for exactly what information should be included and how it should be presented.

Below are some examples of reference list entries for common source types in Harvard style.

• Chapter of a book
• Journal article

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Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:

• Harvard referencing , the most commonly used style in UK universities.
• MHRA , used in humanities subjects.
• APA , used in the social sciences.
• Vancouver , used in biomedicine.
• OSCOLA , used in law.

Your university may have its own referencing style guide.

If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.

References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.

If you don’t acknowledge your sources, you can get in trouble for plagiarism .

To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.

You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.

Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.

Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.

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Home / Guides / Citation Guides / How to Cite Sources

How to Cite Sources

Here is a complete list for how to cite sources. Most of these guides present citation guidance and examples in MLA, APA, and Chicago.

If you’re looking for general information on MLA or APA citations , the EasyBib Writing Center was designed for you! It has articles on what’s needed in an MLA in-text citation , how to format an APA paper, what an MLA annotated bibliography is, making an MLA works cited page, and much more!

MLA Format Citation Examples

The Modern Language Association created the MLA Style, currently in its 9th edition, to provide researchers with guidelines for writing and documenting scholarly borrowings.  Most often used in the humanities, MLA style (or MLA format ) has been adopted and used by numerous other disciplines, in multiple parts of the world.

MLA provides standard rules to follow so that most research papers are formatted in a similar manner. This makes it easier for readers to comprehend the information. The MLA in-text citation guidelines, MLA works cited standards, and MLA annotated bibliography instructions provide scholars with the information they need to properly cite sources in their research papers, articles, and assignments.

• Book Chapter
• Conference Paper
• Documentary
• Encyclopedia
• Google Images
• Kindle Book
• Memorial Inscription
• Museum Exhibit
• Painting or Artwork
• PowerPoint Presentation
• Sheet Music
• Thesis or Dissertation
• YouTube Video

APA Format Citation Examples

The American Psychological Association created the APA citation style in 1929 as a way to help psychologists, anthropologists, and even business managers establish one common way to cite sources and present content.

APA is used when citing sources for academic articles such as journals, and is intended to help readers better comprehend content, and to avoid language bias wherever possible. The APA style (or APA format ) is now in its 7th edition, and provides citation style guides for virtually any type of resource.

Chicago Style Citation Examples

The Chicago/Turabian style of citing sources is generally used when citing sources for humanities papers, and is best known for its requirement that writers place bibliographic citations at the bottom of a page (in Chicago-format footnotes ) or at the end of a paper (endnotes).

The Turabian and Chicago citation styles are almost identical, but the Turabian style is geared towards student published papers such as theses and dissertations, while the Chicago style provides guidelines for all types of publications. This is why you’ll commonly see Chicago style and Turabian style presented together. The Chicago Manual of Style is currently in its 17th edition, and Turabian’s A Manual for Writers of Research Papers, Theses, and Dissertations is in its 8th edition.

Citing Specific Sources or Events

• Declaration of Independence
• Gettysburg Address
• Martin Luther King Jr. Speech
• President Obama’s Farewell Address
• President Trump’s Inauguration Speech
• White House Press Briefing

Additional FAQs

• Citing Archived Contributors
• Citing a Blog
• Citing a Book Chapter
• Citing a Source in a Foreign Language
• Citing an Image
• Citing a Song
• Citing Special Contributors
• Citing a Translated Article
• Citing a Tweet

6 Interesting Citation Facts

The world of citations may seem cut and dry, but there’s more to them than just specific capitalization rules, MLA in-text citations , and other formatting specifications. Citations have been helping researches document their sources for hundreds of years, and are a great way to learn more about a particular subject area.

Ever wonder what sets all the different styles apart, or how they came to be in the first place? Read on for some interesting facts about citations!

1. There are Over 7,000 Different Citation Styles

You may be familiar with MLA and APA citation styles, but there are actually thousands of citation styles used for all different academic disciplines all across the world. Deciding which one to use can be difficult, so be sure to ask you instructor which one you should be using for your next paper.

2. Some Citation Styles are Named After People

While a majority of citation styles are named for the specific organizations that publish them (i.e. APA is published by the American Psychological Association, and MLA format is named for the Modern Language Association), some are actually named after individuals. The most well-known example of this is perhaps Turabian style, named for Kate L. Turabian, an American educator and writer. She developed this style as a condensed version of the Chicago Manual of Style in order to present a more concise set of rules to students.

3. There are Some Really Specific and Uniquely Named Citation Styles

How specific can citation styles get? The answer is very. For example, the “Flavour and Fragrance Journal” style is based on a bimonthly, peer-reviewed scientific journal published since 1985 by John Wiley & Sons. It publishes original research articles, reviews and special reports on all aspects of flavor and fragrance. Another example is “Nordic Pulp and Paper Research,” a style used by an international scientific magazine covering science and technology for the areas of wood or bio-mass constituents.

4. More citations were created on  EasyBib.com  in the first quarter of 2018 than there are people in California.

The US Census Bureau estimates that approximately 39.5 million people live in the state of California. Meanwhile, about 43 million citations were made on EasyBib from January to March of 2018. That’s a lot of citations.

5. “Citations” is a Word With a Long History

The word “citations” can be traced back literally thousands of years to the Latin word “citare” meaning “to summon, urge, call; put in sudden motion, call forward; rouse, excite.” The word then took on its more modern meaning and relevance to writing papers in the 1600s, where it became known as the “act of citing or quoting a passage from a book, etc.”

6. Citation Styles are Always Changing

The concept of citations always stays the same. It is a means of preventing plagiarism and demonstrating where you relied on outside sources. The specific style rules, however, can and do change regularly. For example, in 2018 alone, 46 new citation styles were introduced , and 106 updates were made to exiting styles. At EasyBib, we are always on the lookout for ways to improve our styles and opportunities to add new ones to our list.

Why Citations Matter

Here are the ways accurate citations can help your students achieve academic success, and how you can answer the dreaded question, “why should I cite my sources?”

They Give Credit to the Right People

Citing their sources makes sure that the reader can differentiate the student’s original thoughts from those of other researchers. Not only does this make sure that the sources they use receive proper credit for their work, it ensures that the student receives deserved recognition for their unique contributions to the topic. Whether the student is citing in MLA format , APA format , or any other style, citations serve as a natural way to place a student’s work in the broader context of the subject area, and serve as an easy way to gauge their commitment to the project.

They Provide Hard Evidence of Ideas

Having many citations from a wide variety of sources related to their idea means that the student is working on a well-researched and respected subject. Citing sources that back up their claim creates room for fact-checking and further research . And, if they can cite a few sources that have the converse opinion or idea, and then demonstrate to the reader why they believe that that viewpoint is wrong by again citing credible sources, the student is well on their way to winning over the reader and cementing their point of view.

They Promote Originality and Prevent Plagiarism

The point of research projects is not to regurgitate information that can already be found elsewhere. We have Google for that! What the student’s project should aim to do is promote an original idea or a spin on an existing idea, and use reliable sources to promote that idea. Copying or directly referencing a source without proper citation can lead to not only a poor grade, but accusations of academic dishonesty. By citing their sources regularly and accurately, students can easily avoid the trap of plagiarism , and promote further research on their topic.

They Create Better Researchers

By researching sources to back up and promote their ideas, students are becoming better researchers without even knowing it! Each time a new source is read or researched, the student is becoming more engaged with the project and is developing a deeper understanding of the subject area. Proper citations demonstrate a breadth of the student’s reading and dedication to the project itself. By creating citations, students are compelled to make connections between their sources and discern research patterns. Each time they complete this process, they are helping themselves become better researchers and writers overall.

When is the Right Time to Start Making Citations?

Make in-text/parenthetical citations as you need them.

As you are writing your paper, be sure to include references within the text that correspond with references in a works cited or bibliography. These are usually called in-text citations or parenthetical citations in MLA and APA formats. The most effective time to complete these is directly after you have made your reference to another source. For instance, after writing the line from Charles Dickens’ A Tale of Two Cities : “It was the best of times, it was the worst of times…,” you would include a citation like this (depending on your chosen citation style):

(Dickens 11).

This signals to the reader that you have referenced an outside source. What’s great about this system is that the in-text citations serve as a natural list for all of the citations you have made in your paper, which will make completing the works cited page a whole lot easier. After you are done writing, all that will be left for you to do is scan your paper for these references, and then build a works cited page that includes a citation for each one.

Need help creating an MLA works cited page ? Try the MLA format generator on EasyBib.com! We also have a guide on how to format an APA reference page .

2. Understand the General Formatting Rules of Your Citation Style Before You Start Writing

While reading up on paper formatting may not sound exciting, being aware of how your paper should look early on in the paper writing process is super important. Citation styles can dictate more than just the appearance of the citations themselves, but rather can impact the layout of your paper as a whole, with specific guidelines concerning margin width, title treatment, and even font size and spacing. Knowing how to organize your paper before you start writing will ensure that you do not receive a low grade for something as trivial as forgetting a hanging indent.

Don’t know where to start? Here’s a formatting guide on APA format .

3. Double-check All of Your Outside Sources for Relevance and Trustworthiness First

Collecting outside sources that support your research and specific topic is a critical step in writing an effective paper. But before you run to the library and grab the first 20 books you can lay your hands on, keep in mind that selecting a source to include in your paper should not be taken lightly. Before you proceed with using it to backup your ideas, run a quick Internet search for it and see if other scholars in your field have written about it as well. Check to see if there are book reviews about it or peer accolades. If you spot something that seems off to you, you may want to consider leaving it out of your work. Doing this before your start making citations can save you a ton of time in the long run.

Finished with your paper? It may be time to run it through a grammar and plagiarism checker , like the one offered by EasyBib Plus. If you’re just looking to brush up on the basics, our grammar guides  are ready anytime you are.

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Reference List: Basic Rules

Welcome to the Purdue OWL

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This resourse, revised according to the 7 th  edition APA Publication Manual, offers basic guidelines for formatting the reference list at the end of a standard APA research paper. Most sources follow fairly straightforward rules. However, because sources obtained from academic journals  carry special weight in research writing, these sources are subject to special rules . Thus, this page presents basic guidelines for citing academic journals separate from its "ordinary" basic guidelines. This distinction is made clear below.

Note:  Because the information on this page pertains to virtually all citations, we've highlighted one important difference between APA 6 and APA 7 with an underlined note written in red.  For more information, please consult the   Publication Manual of the American Psychological Association , (7 th  ed.).

Formatting a Reference List

Your reference list should appear at the end of your paper. It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text.

Your references should begin on a new page separate from the text of the essay; label this page "References" in bold, centered at the top of the page (do NOT underline or use quotation marks for the title). All text should be double-spaced just like the rest of your essay.

Basic Rules for Most Sources

• All lines after the first line of each entry in your reference list should be indented one-half inch from the left margin. This is called hanging indentation.
• All authors' names should be inverted (i.e., last names should be provided first).
• For example, the reference entry for a source written by Jane Marie Smith would begin with "Smith, J. M."
• If a middle name isn't available, just initialize the author's first name: "Smith, J."
• Give the last name and first/middle initials for all authors of a particular work up to and including 20 authors ( this is a new rule, as APA 6 only required the first six authors ). Separate each author’s initials from the next author in the list with a comma. Use an ampersand (&) before the last author’s name. If there are 21 or more authors, use an ellipsis (but no ampersand) after the 19th author, and then add the final author’s name.
• Reference list entries should be alphabetized by the last name of the first author of each work.
• For multiple articles by the same author, or authors listed in the same order, list the entries in chronological order, from earliest to most recent.
• Note again that the titles of academic journals are subject to special rules. See section below.
• Italicize titles of longer works (e.g., books, edited collections, names of newspapers, and so on).
• Do not italicize, underline, or put quotes around the titles of shorter works such as chapters in books or essays in edited collections.

Basic Rules for Articles in Academic Journals

• Present journal titles in full.
• Italicize journal titles.
• For example, you should use  PhiloSOPHIA  instead of  Philosophia,  or  Past & Present   instead of  Past and Present.
• This distinction is based on the type of source being cited. Academic journal titles have all major words capitalized, while other sources' titles do not.
• Capitalize   the first word of the titles and subtitles of   journal articles , as well as the   first word after a colon or a dash in the title, and   any proper nouns .
• Do not italicize or underline the article title.
• Deep blue: The mysteries of the Marianas Trench.
• Oceanographic Study: A Peer-Reviewed Publication

Please note:  While the APA manual provides examples of how to cite common types of sources, it does not cover all conceivable sources. If you must cite a source that APA does not address, the APA suggests finding an example that is similar to your source and using that format. For more information, see page 282 of the   Publication Manual of the American Psychological Association , 7 th  ed.

• Directories
• What are citations and why should I use them?
• When should I use a citation?
• Why are there so many citation styles?

Which citation style should I use?

• Chicago Notes Style
• Chicago Author-Date Style
• AMA Style (medicine)
• Bluebook (law)
• Additional Citation Styles
• Built-in Citation Tools
• Quick Citation Generators
• Citation Management Software
• Start Your Research
• Research Guides
• University of Washington Libraries
• Library Guides
• UW Libraries
• Citing Sources

Citing Sources: Which citation style should I use?

The citation style you choose will largely be dictated by the discipline in which you're writing. For many assignments your instructor will suggest or require a certain style. If you're not sure which one to use, it's always best to check with your instructor or, if you are submitting a manuscript, the publisher to see if they require a certain style. In many cases, you may not be required to use any particular style as long as you pick one and use it consistently. If you have some flexibility, use the guide below to help you decide.

Disciplinary Citation Styles

• Social Sciences
• Sciences & Medicine
• Engineering

When in doubt, try: Chicago Notes

• Architecture & Landscape Architecture → try Chicago Notes or Chicago Author-Date
• Art → try Chicago Notes
• Art History → use  Chicago Notes
• Dance → try Chicago Notes or MLA
• Drama → try Chicago Notes or MLA
• Ethnomusicology → try Chicago Notes
• Music → try Chicago Notes
• Music History → use  Chicago Notes
• Urban Design & Planning → try Chicago Notes or Chicago Author-Date

When in doubt, try: MLA

• Cinema Studies → try MLA
• Classics → try Chicago Notes
• English → use  MLA
• History → use   Chicago Notes
• Linguistics → try MLA
• Languages → try MLA
• Literatures → use  MLA
• Philosophy → try MLA
• Religion → try Chicago Notes

When in doubt, try: APA or Chicago Notes

• Anthropology → try Chicago Author-Date
• Business → try APA (see also Citing Business Information from Foster Library)
• Communication → try APA
• Criminology & Criminal Justice → try Chicago Author-Date
• Economics → try APA
• Education → try APA
• Geography → try APA
• Government & Law (for non-law students) → try Chicago Notes
• History → try Chicago Notes
• Informatics → try APA
• Law (for law students) → use Bluebook
• Library & Information Science → try APA
• Museology → try Chicago Notes
• Political Science → try Chicago Notes
• Psychology → use  APA
• Social Work → try APA
• Sociology → use  ASA or Chicago Author-Date

When in doubt, try: CSE Name-Year or CSE Citation-Sequence

• Aquatic & Fisheries Sciences → try CSE Name-Year or APA
• Astronomy → try AIP or CSE Citation-Sequence
• Biology & Life Sciences → try CSE Name-Year or APA
• Chemistry → try ACS
• Earth & Space Sciences → try CSE Name-Year or APA
• Environmental Sciences → try CSE Name-Year or APA
• Forest Sciences → try CSE Name-Year or APA
• Health Sciences: Public Health, Medicine, & Nursing → use AMA or NLM
• Marine Sciences → try CSE Name-Year or APA
• Mathematics → try AMS or CSE Citation-Sequence
• Oceanography → try CSE Name-Year or APA
• Physics → try AIP or CSE Citation-Sequence
• Psychology  → use  APA

When in doubt, try: CSE Name-Year or IEEE

• Aeronautics and Astronautics → try CSE Citation-Sequence
• Bioengineering → try AMA or NLM
• Chemical Engineering → try ACS
• Civil and Environmental Engineering → try CSE Name-Year
• Computational Linguistics → try CSE Citation-Sequence
• Computer Science & Engineering → try IEEE
• Electrical and Computer Engineering → try IEEE
• Engineering (general) → try IEEE or CSE Name-Year
• Human Centered Design & Engineering → try IEEE
• Human-Computer Interaction + Design → try IEEE
• Industrial and Systems Engineering → try CSE Name-Yea r
• Mechanical Engineering → try Chicago Notes or Chicago Author-Date

See also: Additional Citation Styles , for styles used by specific engineering associations.

Pro Tip: Citation Tools Save Time & Stress!

If you’re enrolled in classes that each require a different citation style, it can get confusing really fast! The tools on the Quick Citation Generators section can help you format citations quickly in many different styles.

• << Previous: Why are there so many citation styles?
• Next: Citation Style Guides >>
• Last Updated: May 1, 2024 12:48 PM
• URL: https://guides.lib.uw.edu/research/citations

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Writing Research Papers

• What Types of References Are Appropriate?

When writing a research paper, there are many different types of sources that you might consider citing.  Which are appropriate?  Which are less appropriate?  Here we discuss the different types of sources that you may wish to use when working on a research paper.   

Please note that the following represents a general set of recommended guidelines that is not specific to any class and does not represent department policy.  The types of allowable sources may vary by course and instructor.

Highly appropriate: peer-reviewed journal articles

In general, you should primarily cite peer-reviewed journal articles in your research papers.  Peer-reviewed journal articles are research papers that have been accepted for publication after having undergone a rigorous editorial review process.  During that review process, the article was carefully evaluated by at least one journal editor and a group of reviewers (usually scientists that are experts in the field or topic under investigation).  Often the article underwent revisions before it was judged to be satisfactory for publication. 

Most articles submitted to high quality journals are not accepted for publication.  As such, research that is successfully published in a respected peer-reviewed journal is generally regarded as higher quality than research that is not published or is published elsewhere, such as in a book, magazine, or on a website.  However, just because a study was published in a peer-reviewed journal does not mean that it is free from error or that its conclusions are correct.  Accordingly, it is important to critically read and carefully evaluate all sources, including peer-reviewed journal articles.

Tips for finding and using peer-reviewed journal articles:

• Many databases, such as PsycINFO, can be set to only search for peer-reviewed journal articles. Other search engines, such as Google Scholar, typically include both peer-reviewed and not peer-reviewed articles in search results, and thus should be used with greater caution. 
• Even though a peer-reviewed journal article is, by definition, a source that has been carefully vetted through an editorial process, it should still be critically evaluated by the reader. 

Potentially appropriate: books, encyclopedias, and other scholarly works

Another potential source that you might use when writing a research paper is a book, encyclopedia, or an official online source (such as demographic data drawn from a government website).  When relying on such sources, it is important to carefully consider its accuracy and trustworthiness.  For example, books vary in quality; most have not undergone any form of review process other than basic copyediting.  In many cases, a book’s content is little more than the author’s informed or uninformed opinion. 

However, there are books that have been edited prior to publication, as is the case with many reputable encyclopedias; also, many books from academic publishers are comprised of multiple chapters, each written by one or more researchers, with the entire volume carefully reviewed by one or more editors.  In those cases, the book has undergone a form of peer review, albeit often not as rigorous as that for a peer-reviewed journal article.

Tips for using books, encyclopedias, and other scholarly works:

• When using books, encyclopedias, and other scholarly works (that is, works written or produced by researchers, official agencies, or corporations), it is important to very carefully evaluate the quality of that source.
• If the source is an edited volume (in which case in the editor(s) will be listed on the cover), is published by a reputable source (such as Academic Press, MIT Press, and others), or is written by a major expert in the field (such as a researcher with a track record of peer-reviewed journal articles on the subject), then it is more likely to be trustworthy.
• For online encyclopedias such as Wikipedia, an instructor may or may not consider that an acceptable source (by default, don’t assume that a non-peer reviewed source will be considered acceptable). It is best to ask the instructor for clarification. 1

Usually inappropriate: magazines, blogs, and websites  

Most research papers can be written using only peer-reviewed journal articles as sources.  However, for many topics it is possible to find a plethora of sources that have not been peer-reviewed but also discuss the topic.  These may include articles in popular magazines or postings in blogs, forums, and other websites.  In general, although these sources may be well-written and easy to understand, their scientific value is often not as high as that of peer-reviewed articles.  Exceptions include some magazine and newspaper articles that might be cited in a research paper to make a point about public awareness of a given topic, to illustrate beliefs and attitudes about a given topic among journalists, or to refer to a news event that is relevant to a given topic. 

Tips for using magazines, blogs, and websites:

• Avoid such references if possible. You should primarily focus on peer-reviewed journal articles as sources for your research paper.  High quality research papers typically do not rely on non-academic and not peer-reviewed sources.
• Refer to non-academic, not peer-reviewed sources sparingly, and if you do, be sure to carefully evaluate the accuracy and scientific merit of the source.

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

Databases and Search Engines (may require connection to UCSD network)

• Google Scholar
• PubMed (NIH/NLM)
• Web of Science  

UCSD Resources on Finding and Evaluating Sources

• UCSD Library Databases A-Z
• UCSD Library Psychology Research Guide: Start Page
• UCSD Library Psychology Research Guide : Finding Articles
• UCSD Library Psychology Research Guide : Evaluating Sources

External Resources

• Critically Reading Journal Articles from PSU/ Colby College
• How to Seriously Read a Journal Article from Science Magazine
• How to Read Journal Articles from Harvard University
• How to Read a Scientific Paper Infographic from Elsevier Publishing
• Tips for searching PsycINFO from UC Berkeley Library
• Tips for using PsycINFO effectively from the APA Student Science Council

1 Wikipedia articles vary in quality; the site has a peer review system and the very best articles ( Featured Articles ), which go through a multi-stage review process, rival those in traditional encyclopedias and are considered the highest quality articles on the site.

Prepared by s. c. pan for ucsd psychology, graphic adapted from  t-x-generic-apply.svg , a public domain creation by the tango desktop project..

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• Research Paper Structure
• Formatting Research Papers
• Using Databases and Finding References
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

Citation Generator

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What Is Cite This For Me’s Citation Generator?

Cite This For Me’s open-access generator is an automated citation machine that turns any of your sources into citations in just a click. Using a citation generator helps students to integrate referencing into their research and writing routine; turning a time-consuming ordeal into a simple task.

A citation machine is essentially a works cited generator that accesses information from across the web, drawing the relevant information into a fully-formatted bibliography that clearly presents all of the sources that have contributed to your work.

If you don’t know how to cite correctly, or have a fast-approaching deadline, Cite This For Me’s accurate and intuitive citation machine will lend you the confidence to realise your full academic potential. In order to get a grade that reflects all your hard work, your citations must be accurate and complete. Using a citation maker to create your references not only saves you time but also ensures that you don’t lose valuable marks on your assignment.

Not sure how to format your citations, what citations are, or just want to find out more about Cite This For Me’s citation machine? This guide outlines everything you need to know to equip yourself with the know-how and confidence to research and cite a wide range of diverse sources in your work.

Why Do I Need To Cite?

Simply put, referencing is the citing of sources used in essays, articles, research, conferences etc. When another source contributes to your work, you have to give the original owner the appropriate credit. After all, you wouldn’t steal someone else’s possessions so why would you steal their ideas?

Any factual material or ideas you take from another source must be acknowledged in a reference, unless it is common knowledge (e.g. President Kennedy was killed in 1963). Failing to credit all of your sources, even when you’ve paraphrased or completely reworded the information, is plagiarism. Plagiarizing will result in disciplinary action, which can range from losing precious points on your assignment to expulsion from your university.

What’s more, attributing your research infuses credibility and authority into your work, both by supporting your own ideas and by demonstrating the breadth of your research. For many students, crediting sources can be a confusing and tedious process, but it’s a surefire way to improve the quality of your work so it’s essential to get it right. Luckily for you, using Cite This For Me’s citation machine makes creating accurate references easier than ever, leaving more time for you to excel in your studies.

In summary, the referencing process serves three main functions:

• To validate the statements and conclusions in your work by providing directions to other sound sources that support and verify them.
• To help your readers locate, read and check your sources, as well as establishing their contribution to your work.
• To give credit to the original author and hence avoid committing intellectual property theft (known as ‘plagiarism’ in academia).

How Do I Cite My Sources With The Cite This For Me Citation Machine?

Cite This For Me’s citation generator is the most accurate citation machine available, so whether you’re not sure how to format in-text citations or are looking for a foolproof solution to automate a fully-formatted works cited list, this citation machine will solve all of your referencing needs.

Referencing your source material doesn’t just prevent you from losing valuable marks for plagiarism, it also provides all of the information to help your reader find for themselves the book, article, or other item you are citing. The accessible interface of this citation builder makes it easy for you to identify the source you have used – simply enter its unique identifier into the citation machine search bar. If this information is not available you can search for the title or author instead, and then select from the search results that appear below the citation generator.

The good news is that by using tools such as Cite This For Me, which help you work smarter, you don’t need to limit your research to sources that are traditional to cite. In fact, there are no limits to what you can reference, whether it be a YouTube video, website or a tweet.

To use the works cited generator, simply:

• Select from APA, MLA, Chicago, ASA, IEEE and AMA * styles.
• Choose the type of source you would like to cite (e.g. website, book, journal, video).
• Enter the URL , DOI , ISBN , title, or other unique source information into the citation generator to find your source.
• Click the ‘Cite’ button on the citation machine.
• Copy your new reference from the citation generator into your bibliography or works cited list.
• Repeat for each source that has contributed to your work.

*If you require another referencing style for your paper, essay or other academic work, you can select from over 1,000 styles by creating a free Cite This For Me account.

Once you have created your Cite This For Me account you will be able to use the citation machine to generate multiple references and save them into a project. Use the highly-rated iOS or Android apps to create references in a flash with your smartphone camera, export your complete bibliography in one go, and much more.

What Will The Citation Machine Create For Me?

Cite This For Me’s citation maker will generate your reference in two parts; an in-text citation and a full reference to be copied straight into your work.

The citation machine will auto-generate the correct formatting for your works cited list or bibliography depending on your chosen style. For instance, if you select a parenthetical style on the citation machine it will generate an in-text citation in parentheses, along with a full reference to slot into your bibliography. Likewise, if the citation generator is set to a footnote style then it will create a fully-formatted reference for your reference page and bibliography, as well as a corresponding footnote to insert at the bottom of the page containing the relevant source.

Parenthetical referencing examples:

In-text example: A nation has been defined as an imagined community (Anderson, 2006).* Alternative format: Anderson (2006) defined a nation as an imagined community.

*The citation machine will create your references in the first style, but this should be edited if the author’s name already appears in the text.

Bibliography / Works Cited list example: Anderson, B. (2006). Imagined Communities. London: Verso.

Popular Citation Examples

• Citing archive material
• Citing artwork
• Citing an audiobook
• Citing the Bible
• Citing a blog
• Citing a book
• Citing a book chapter
• Citing a comic book
• Citing conference proceedings
• Citing a court case
• Citing a database
• Citing a dictionary entry
• Citing a dissertation
• Citing an eBook
• Citing an edited book
• Citing an email
• Citing an encyclopedia article
• Citing a government publication
• Citing an image
• Citing an interview
• Citing a journal article
• Citing legislation
• Citing a magazine
• Citing a meme
• Citing a mobile app
• Citing a movie
• Citing a newspaper
• Citing a pamphlet
• Citing a patent
• Citing a play
• Citing a podcast
• Citing a poem
• Citing a presentation
• Citing a press release
• Citing a pseudonym
• Citing a report
• Citing Shakespeare
• Citing social media
• Citing a song
• Citing software
• Citing a speech
• Citing translated book
• Citing a TV Show
• Citing a weather report
• Citing a website
• Citing Wikipedia article
• Citing a YouTube video

What Are Citation Styles?

A citation style is a set of rules that you, as an academic writer, must follow to ensure the quality and relevance of your work. There are thousands of styles that are used in different academic institutions around the world, but in the US the most common are APA, MLA and Chicago.

The style you need to use will depend on the preference of your professor, discipline or academic institution – so if you’re unsure which style you should be using, consult your department and follow their guidelines exactly, as this is what you’ll be evaluated on when it comes to grading.

Referencing isn’t just there to guard against plagiarism – presenting your research in a clear and consistent way eases the reader’s comprehension. Each style has a different set of rules for both page formatting and referencing. Be sure to adhere to formatting rules such as font type, font size and line spacing to ensure that your work is easily legible. Furthermore, if your work is published as part of an anthology or collected works, each entry will need to be presented in the same style to maintain uniformity throughout. It is important to make sure that you don’t jump from one style to another, so follow the rules carefully to ensure your reference page and bibliography are both accurate and complete.

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• Published: 27 May 2024

Using machine learning algorithms to enhance IoT system security

• Hosam El-Sofany 1 ,
• Samir A. El-Seoud 2 ,
• Omar H. Karam 2 &
• Belgacem Bouallegue 1 , 3  

Scientific Reports volume  14 , Article number:  12077 ( 2024 ) Cite this article

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The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

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Introduction.

Technology such as cloud computing, cloud edge, and software-defined networking (SDN) have significantly increased users’ reliance on their infrastructure. Consequently, the number of threats faced by these users has also risen. As a result, security management during IoT system development has become increasingly difficult and complex. The IoT can be described as an electrical network that connects physical objects, such as sensors, with software that makes it possible for them to exchange, examine, and gather data. Various sectors use IoT applications, including the military, personal healthcare, household appliances, and agriculture production infrastructure 1 . This research attempts to achieve the Sustainable Cities and Communities Goal (SDG 11) included in the UN Sustainable Development Goals (SDG) 2 . Addressing the challenges and finding solutions for the IoT require considering a wide range of factors. It is crucial for solutions to encompass the entire system to provide comprehensive security. However, most IoT devices operate without human interaction, making them susceptible to unauthorized access. Therefore, it is imperative to enhance the existing security techniques to safeguard the IoT environment 3 . ML techniques can offer potential alternatives for securing IoT systems, including:

Intrusion detection and prevention ML can create IoT intrusion detection and prevention (IDPS) tools. ML algorithms can analyze network traffic, device logs, and other data related to known attacks or suspicious activity.

Anomaly detection ML algorithms can learn IoT device behavior and network interactions through anomaly detection. ML models can detect unusual IoT activity using real-time data. This helps detect security breaches like unauthorized access or malicious acts and prompt appropriate responses.

Threat intelligence and prediction ML can analyze big security data sets and provide insights. ML models may discover new risks, anticipate attack pathways, and give actionable insight to IoT security practitioners by analyzing data from security feeds, vulnerability databases, and public forums.

Firmware and software vulnerability analysis Researchers may use ML to analyze IoT firmware and software for vulnerabilities. ML models may discover IoT device firmware and software security problems by training on known vulnerabilities and coding patterns. This helps manufacturers repair vulnerabilities before deployment or deliver security patches quickly.

Behavior-based authentication ML algorithms can learn IoT devices and user behavior. By analyzing device usage patterns, ML models may create predictable behavior profiles. ML can require extra authentication or warn for illegal access when a device or user deviates considerably from the learned profile.

Data privacy and encryption ML can assist in ensuring data privacy and security in IoT systems. ML algorithms may provide homomorphic encryption, which permits calculations on encrypted data. ML can perform data anonymization and de-identification to safeguard sensitive data and facilitate analysis and insights.

In general, ML techniques must be used in conjunction with other security measures to offer complete security for IoT systems. ML algorithms and methods have been applied in various tasks, including machine translation, regression, clustering, transcription, detection, classification, probability mass function, sampling, and estimation of probability density. Numerous applications utilize ML techniques and algorithms, such as spam identification, image and video recognition, customer segmentation, sentiment analysis, demand forecasting, virtual personal assistants, detection of fraudulent transactions, automation of customer service, authentication, malware detection, and speech recognition 4 .

In addition, IoT and ML integration can enhance the devices of IoT levels of security, thereby increasing their reliability and accessibility. ML’s advanced data exploration methods play an important role in elevating IoT security from only providing security for communication devices to intelligent systems with a high level of security 5 .

ML-based models have emerged as a response to cyberattacks within the IoT ecosystem, and the combination of Deep Learning (DL) and ML approaches represents a novel and significant development that requires careful consideration. Numerous uses, including wearable smart gadgets, smart homes, healthcare, and Vehicular Area Networks (VANET), necessitate the implementation of robust security measures to safeguard user privacy and personal information. The successful utilization of IoT is evident across multiple sectors of modern life 6 . By 2025, we expect that the IoT will have an economic effect of $2.70–$6.20 trillion. Research findings indicate that ML and DL techniques are key drivers of automation in knowledge work, thereby contributing to the economic impact. There have been many recent technological advancements that are shaping our world in significant ways. By 2025, we expect an estimated $5.2–$6.7 trillion in annual economic effects from knowledge labor automation 7 .

This research study addresses the vulnerabilities in IoT systems by presenting a novel ML-based security model. The proposed approach aims to address the increasing security concerns associated with the Internet of Things. The study analyzes recent technologies, security, intelligent solutions, and vulnerabilities in IoT-based smart systems that utilize ML as a crucial technology to enhance IoT security. The paper provides a detailed analysis of using ML technologies to improve IoT systems’ security and highlights the benefits and limitations of applying ML in an IoT environment. When compared to current ML-based models, the proposed approach outperforms them in both accuracy and execution time, making it an ideal option for improving the security of IoT systems. The creation of a novel ML-based security model, which can enhance the effectiveness of cybersecurity systems and IoT infrastructure, is the contribution of the study. The proposed model can keep threat knowledge databases up to date, analyze network traffic, and protect IoT systems from newly detected attacks by drawing on prior knowledge of cyber threats.

The study comprises five sections: “ Related works ” section presents a summary of some previous research. “ IoT, security, and ML ” section introduces the Internet of Things’ security and ML aspects. “ The proposed IoT framework architecture ” section presents the proposed IoT framework architecture, providing detailed information and focusing on its performance evaluation. “ Result evaluation and discussion ” section provides an evaluation of the outcomes and compares them with other similar systems. We achieve this by utilizing appropriate datasets, methodologies, and classifiers. “ Conclusions and upcoming work ” section concludes the discussion and outlines future research directions.

Related works

The idea of security in IoT devices has been recently articulated in studies that analyze the security needs at several layers of architecture, such as the application, cloud, network, data, and physical layers. Layers have examined potential vulnerabilities and attacks against IoT devices, classified IoT attacks, and explained layer-based security requirements 8 . On the other hand, industrial IoT (IIoT) networks are vulnerable to cyberattacks. Developing IDS is important to secure IIoT networks. The authors presented three DL models, LSTM, CNN, and a hybrid, to identify IIoT network breaches 9 . The researchers used the UNSW-NB15 and X-IIoTID datasets to identify normal and abnormal data, then compared them to other research using multi-class, and binary classification. The hybrid LSTM + CNN model has the greatest intrusion detection accuracy in both datasets. The researchers also assessed the implemented models’ accuracy in detecting attack types in the datasets 9 .

In Ref. 10 , the authors introduced the hybrid synchronous-asynchronous privacy-preserving federated technique. The federated paradigm eliminates FL-enabled NG-IoT setup issues and protects all its pieces with Two-Trapdoor Homomorphic Encryption. The server protocol blocks irregular users. The asynchronous hybrid LEGATO algorithm reduces user dropout. By sharing data, they assist data-poor consumers. In the presented model, security analysis ensures federated correctness, auditing, and PP. Their performance evaluation showed higher functionality, accuracy, and reduced system overheads than peer efforts. For medical devices, the authors of Ref. 11 developed an auditable privacy-preserving federated learning (AP2FL) method. By utilizing Trusted Execution Environments (TEEs), AP2FL reduces issues about data leakage during training and aggregation activities on both servers and clients. The authors of this study aggregated user updates and found data similarities for non-IID data using Active Personalized Federated Learning (ActPerFL) and Batch Normalization (BN).

In Ref. 12 , the authors addressed two major consumer IoT threat detection issues. First, the authors addressed FL’s unfixed issue: stringent client validation. They solved this using quantum-centric registration and authentication, ensuring strict client validation in FL. FL client model weight protection is the second problem. They suggested adding additive homomorphic encryption to their model to protect FL participants’ privacy without sacrificing computational speed. This technique obtained an average accuracy of 94.93% on the N-baIoT dataset and 91.93% on the Edge-IIoTset dataset, demonstrating consistent and resilient performance across varied client settings.

Utilizing a semi-deep learning approach, SteelEye was created in Ref. 13 to precisely detect and assign responsibility for cyberattacks that occur at the application layer in industrial control systems. The proposed model uses category boosting and a diverse range of variables to provide precise cyber-attack detection and attack attribution. SteelEye demonstrated superior performance in terms of accuracy, precision, recall, and Fl-score compared to state-of-the-art cyber-attack detection and attribution systems.

In Ref. 14 , researchers developed a fuzzy DL model, an enhanced adaptive neuro-fuzzy inference system (ANFIS), fuzzy matching (FM), and a fuzzy control system to detect network risks. Our fuzzy DL finds robust nonlinear aggregation using the fuzzy Choquet integral. Metaheuristics optimized ANFIS attack detection’s error function. FM verifies transactions to detect blockchain fraud and boost efficiency. The first safe, intelligent fuzzy blockchain architecture, which evaluates IoT security threats and uncertainties, enables blockchain layer decision-making and transaction approval. Tests show that the blockchain layer’s throughput and latency can reveal threats to blockchain and IoT. Recall, accuracy, precision, and F1-score are important for the intelligent fuzzy layer. In blockchain-based IoT networks, the FCS model for threat detection was also shown to be reliable.

In Ref. 15 , the study examined Federated Learning (FL) privacy measurement to determine its efficacy in securing sensitive data during AI and ML model training. While FL promises to safeguard privacy during model training, its proper implementation is crucial. Evaluation of FL privacy measurement metrics and methodologies can identify gaps in existing systems and suggest novel privacy enhancement strategies. Thus, FL needs full research on “privacy measurement and metrics” to thrive. The survey critically assessed FL privacy measurement found research gaps, and suggested further study. The research also included a case study that assessed privacy methods in an FL situation. The research concluded with a plan to improve FL privacy via quantum computing and trusted execution environments.

IoT, security, and ML

Iot attacks and security vulnerabilities.

Critical obstacles standing in the way of future attempts to see IoT fully accepted in society are security flaws and vulnerabilities. Everyday IoT operations are successfully managed by security concerns. In contrast, they have a centralized structure that results in several vulnerable points that may be attacked. For example, unpatched vulnerabilities in IoT devices are a security concern due to outdated software and manual updates. Weak authentication in IoT devices is a significant issue due to easy-to-identify passwords. Attackers commonly target vulnerable Application Programming Interfaces (APIs) in IoT devices using code injections, a man-in-the-middle (MiTM), and Distributed Denial-of-Service (DDoS) 16 . Unpatched IoT devices pose risks to users, including data theft and physical harm. IoT devices store sensitive data, making them vulnerable to theft. In the medical field, weak security in devices such as heart monitors and pacemakers can impede medical treatment. Figure  1 illustrates the types of IoT attacks (threats) 17 . Unsecured IoT devices can be taken over and used in botnets, leading to cyberattacks such as DDoS, spam, and phishing. The Mirai software in 2016 encouraged criminals to develop extensive botnets for IoT devices, leading to unprecedented attacks. Malware can easily exploit weak security safeguards in IoT devices 18 . Because there are so many connected devices, it may be difficult to ensure IoT device security. Users must follow fundamental security practices, such as changing default passwords and prohibiting unauthorized remote access 19 . Manufacturers and vendors must invest in securing IoT tool managers by proactively notifying users about outdated software, enforcing strong password management, disabling remote access for unnecessary functions, establishing strict API access control, and protecting command-and-control (C&C) servers from attacks.

Types of IoT attacks.

IoT applications’ support security issues

Security is a major requirement for almost all IoT applications. IoT applications are expanding quickly and have impacted current industries. Even though operators supported some applications with the current technologies of networks, others required greater security support from the IoT-based technologies they use 20 . The IoT has several uses, including home automation and smart buildings and cities. Security measures can enhance home security, but unauthorized users may damage the owner’s property. Smart applications can threaten people’s privacy, even if they are meant to raise their standard of living. Governments are encouraging the creation of intelligent cities, but the safety of citizens’ personal information may be at risk 21 , 22 .

Retail extensively uses the IoT to improve warehouse restocking and create smart shopping applications. Augmented reality applications enable offline retailers to try online shopping. However, security issues have plagued IoT apps implemented by retail businesses, leading to financial losses for both clients and companies. Hackers may access IoT apps to provide false details regarding goods and steal personal information 23 . Smart agriculture techniques include selective irrigation, soil hydration monitoring, and temperature and moisture regulation. Smart technologies can result in larger crops and prevent the growth of mold and other contaminants. IoT apps monitor farm animals’ activity and health, but compromised agriculture applications can lead to the theft of animals and damage to crops. Intelligent grids and automated metering use smart meters to monitor and record storage tanks, improve solar system performance, and track water pressure. However, smart meters are more susceptible to cyber and physical threats than traditional meters. Advanced Metering Infrastructure (AMI) connects all electrical appliances in a house to smart meters, enabling communication and security networks to monitor consumption and costs. Adversary incursions into such systems might change the data obtained, costing consumers or service providers money 24 . IoT apps in security and emergency sectors limit access to restricted areas and identify harmful gas leaks. Security measures protect confidential information and sensitive products. However, compromised security in IoT apps can have disastrous consequences, such as criminals accessing banned areas or erroneous radiation level alerts leading to serious illnesses 25 .

IoT security attacks based on each layer

IoT devices’ architecture includes five layers: perception, network Layer, middleware (information processing), application, and business (system management). Figure  2 illustrates how the development of IoT ecosystems has changed from a three-layer to a five-layer approach. IoT threats can be physical or cyber, with cyberattacks being passive or active. IoT devices can be physically damaged by attacks, and various IoT security attacks based on each tier are described 26 . Perception layer attacks are intrusions on IoT physical components, for example, devices and sensors. Some of the typical perception layer attacks are as follows:

Botnets Devices get infected by malware called botnets, like Mirai. The bot’s main objectives are to infect improperly configured devices and assault a target server when given the order by a botmaster 27 .

Sleep deprivation attack Attacks from sleep deprivation are linked to battery-powered sensor nodes and equipment. Keeping the machines and devices awake for a long time is the aim of the sleep disturbances assault 28 .

Node tampering and jamming Node tampering attacks are launched by querying the machines to acquire accessibility to and change confidential data, like routing data tables and cryptographic shared keys. A node jamming assault, on the other hand, occurs when perpetrators breach the radio frequencies of wireless sensor nodes 29 .

Eavesdropping By allowing the attacker to hear the information being transferred across a private channel, eavesdropping is an exploit that puts the secrecy of a message in danger 30 .

IoT ecosystem five-layer architecture.

These attacks can harm most or all IoT system physical components and can be prevented by implementing appropriate security measures.

Network layer attacks aim to interfere with the IoT space’s network components, which include routers, bridges, and others. The following are some examples of network layer attacks:

Man-in-the-middle (MiTM) This threat involves an attacker posing as a part of the communication networks and directly connecting to another user device 31 .

Denial of service (DoS) Attackers who use DoS techniques generate numerous pointless requests, making it challenging for the user to access and utilize IoT gadgets.

Routing attacks Malicious nodes engage in routing-type assaults to block routing functionality or to perform DoS activities.

Middleware attacks An assault on middleware directly targets the IoT system’s middleware components. Cloud-based attacks, breaches of authentication, and signature packaging attacks are the three most common forms of middleware attacks.

These attacks can be prevented by implementing appropriate security measures.

Smart cities, smart grids, and smart homes are some examples of apps included in the application layer. An application layer attack relates to the security flaws in IoT apps. Here are a few examples of application layer attacks 32 :

Malware The use of executable software by attackers to interfere with network equipment is known as malware.

Phishing attack This is a sort of breach that seeks to get users’ usernames and passwords by making them appear to be reliable entities.

Code injection attack The main goal of an injector attack into a program or script code is to inject an executable code into the memory space of the breached process.

Appropriate security measures can help prevent these attacks as well.

Overview of ML within the IoT

IoT systems are susceptible to hackers because they lack clear boundaries and new devices are always being introduced. There is a possibility to create algorithms that can learn about the behavior of objects and other IoT components inside such large networks by utilizing ML and DL approaches. By using these techniques to predict a system’s expected behavior based on past experiences, security protocols can be developed to a significant extent.

ML techniques and their applications in IoT

ML techniques play an essential role in analyzing and extracting insights from the massive amount of data produced by IoT devices. Here are some popular ML techniques and their applications in the IoT:

Supervised learning This type of algorithm learns from labeled training data. Various applications in the IoT can utilize supervised learning, such as:

Anomaly detection By training ML models to recognize abnormal patterns or behaviors in IoT sensor data, we can identify anomalies or potential security breaches.

Predictive maintenance By analyzing past sensor data, supervised learning algorithms can predict equipment failures or maintenance requirements. This enables the implementation of proactive maintenance measures, leading to a decrease in downtime.

Environmental monitoring ML models can learn from sensor data to predict environmental conditions like air quality, water pollution, or weather patterns.

Unsupervised learning Unsupervised learning algorithms extract patterns or structures from unlabeled data without predefined categories. In IoT, unsupervised learning techniques find applications such as:

Clustering ML models can group similar IoT devices or data points, facilitating resource allocation, load balancing, or identifying network segments.

Dimensionality reduction Unsupervised learning techniques like autoencoders or principal component analysis (PCA) make it easier to analyze IoT data.

Behavioral profiling Unsupervised learning can help in understanding the normal behavior of IoT devices or users, enabling the detection of deviations or anomalies.

Reinforcement learning Reinforcement learning aims to maximize a reward by training an agent how to interact with its environment and use feedback to improve its performance. The following applications use reinforcement learning on the IoT.

Energy management ML models can learn optimal energy allocation strategies for IoT devices to maximize energy efficiency or minimize costs.

Adaptive IoT systems Reinforcement learning can be used to optimize IoT system parameters or configurations based on real-time feedback and changing conditions.

Smart resource allocation ML models can learn to allocate resources dynamically based on demand, user preferences, or changing network conditions.

Deep learning DL algorithms, especially deep neural networks, excel at processing complex data and extracting high-level features. In IoT, DL has various applications, including:

Image and video analysis DL models can analyze images or video streams from IoT devices, enabling applications like object detection, surveillance, or facial recognition.

Natural language processing (NLP) DL techniques can process and understand text or voice data from IoT devices, enabling voice assistants, sentiment analysis, or chatbots.

Time-series analysis DL models, such as long short-term memory (LSTM) or recurrent neural networks (RNNs) networks, can analyze time-series sensor datasets for predicting future values or detecting anomalies.

ML for IoT security

ML is a promising approach for defending IoT devices against cyberattacks. It offers a unique strategy for thwarting assaults and provides several benefits, including designing sensor-dependent systems, providing real-time evaluation, boosting security, reducing the flowing data, and utilizing the large quantity of data on the Internet for all individualized user applications. The influence of ML on the IoT’s development is crucial for enhancing practical smart applications. ML has garnered scientific attention recently and is being applied to IoT security as well as the growth of numerous other industries. Effective data exploration methods for identifying “abnormal” and “normal” IoT components and behavior of devices inside the IoT ecosystem are DL and ML. Consequently, to transform the security of IoT systems from enabling secure Device-to-Device (D2D) connectivity to delivering intelligence security-based systems, ML/DL techniques are needed 33 .

Enhancing IoT security using the algorithms of ML

ML approaches, such as ensemble learning, k-means clustering, Random Forest (RF), Association Rule (AR), Decision Tree (DT), AdaBoost, Support Vector Machine (SVM), XGBoost, and K-Nearest Neighbor (KNN), have benefits, drawbacks, and applications in IoT security. DT, a natural ML technique, resembles a tree, with branches and leaves that serve as nodes in the model. In classification, SVM maximizes the distance between the closest points and the hyperplane to classify the class 34 . In identifying DDoS attacks, RF performs better than SVM, ANN, and KNN. A Principal Component Analysis (PCA) with KNN and classifier softmax has been suggested in Ref. 35 to develop a system that has great time efficiency while still having cheap computation, which enables it to be employed in IoT real-time situations.

Limitations of applying ML in networks of IoT

Using ML approaches for IoT networks has limitations because of dedicated processing power and IoT machines’ limited energy. IoT networks generate data with a variety of structures, forms, and meanings, and traditional ML algorithms are ill-equipped to handle these massive, continuous streams of real-time data. The semantic and syntactic variability in this data is evident, particularly in the case of huge data, and heterogeneous datasets with unique features pose problems for effective and uniform generalization. ML assumes that all the dataset’s statistical attributes are constant, and the data must first go through preprocessing and cleaning before fitting into a particular model. However, in the real world, data comes from multiple nodes and has different representations with variant formatting, which presents challenges for ML algorithms 36 .

The proposed IoT framework architecture

Fundamental concepts and methodologies.

Software defined networking (SDN) SDN is a cutting-edge networking model that separates the data plane from the control plane. This improves network programmability, adaptability, and management, and it also enables external applications to control how the network behaves. The SDN’s three basic components are communication interfaces, controllers, and switches. Cognitive judgments were imposed on the switches by a central authority (i.e., the SDN controller). It keeps the state of the system up to date by changing the flow rules of the appropriate switches. IoT systems’ success and viability depend on SDN adoption. To handle IoT networks’ huge data flows and minimize bottlenecks, SDN’s routing traffic intelligence and improving usage of the network are essential. This connection may be applied at many layers in the IoT network, including enabling end-to-end IoT traffic control, core, access, and cloud networks (where creation, processing, and providing of data takes place). SDN also enhances IoT security, for example, tenant traffic isolation, tracking centralized security based on the network’s global view, and dropping of traffic at the edge of the network to ward off malignant traffic.

Network function virtualization (NFV) Virtualization in network contexts is called network function virtualization (NFV). NFV separates software from hardware, adding value and reducing capital and operational costs. The European Telecommunications Standards Institute (ETSI) has standardized this approach’s novel design for use in telecommunications systems. The architecture of ETSI NFV has three basic components:

Virtualization infrastructure Virtualization technologies are found in this layer in addition to needed hardware that offers abstractions to resources for Virtualized Network Functions (VNFs). Cloud platforms handle networking, data processing, and storage.

Virtual network functions VNFs replace specific hardware equipment for network functions. They scale and cost-effectively handle network services across numerous settings.

Management and orchestration Block of Management and orchestration (MANO) is a component of ETSI NFV and is responsible for communicating with the VNF layer and the infrastructure layer. It manages monitoring VNFs, configuration, instantiation, and global resource allocation.

The ecosystem of the IoT is given value by virtualized resources of the network, explaining its variability and quick expansion. NFV and SDN can offer advanced virtual monitoring tools like Deep Packet Inspectors (DPIs) and Intrusion Detection Systems (IDSs). They can provide scalable network security equipment, as well as deploy and configure on-demand components, such as authentication systems and firewalls, to defend against attacks that have been identified by monitoring agents. When processing for security is offloaded from resource-constrained IoT devices to virtual instances, the resulting boost in efficiency and drop in energy consumption clear the way for other useful applications to be implemented. IoT security hardware lacks NFV’s flexibility and enhanced security. NFV’s value-added features improved IoT security, even if they did not replace current solutions.

Machine learning (ML) ML is an algorithmic artificial intelligence (AI) discipline that uses techniques to give intelligence to devices and computers. ML methods include unsupervised , supervised , and reinforcement learning. They are typically used in the security of networks. ML is used to specify and precisely identify the security regulations of the data plane. In mitigating a sort of attack given by tagging traffic networks or creating policies to access control, the difficulty is to fine-tune key security protocol parameters. Moreover, several ML approaches may prevent IoT attacks.

Supervised learning In algorithms of supervised learning, the model output is known even though the underlying relationships between the data are unknown. This model is often trained with two datasets: One for “testing” and “evaluating” the driven model and another to “learn” from. Within the context of security, it is common to compare a suspected attack to a database of known threats.

Unsupervised learning Data is not pre-labeled, and the model is unknown. It sets it apart from supervised learning. It aims to classify and find patterns in the data.

Reinforcement learning It looks at problems and methods to enhance its model through study. It employs trial and error and incentive mechanisms to train its models in a novel way. A metric known as the “value function” is determined by tracking the output’s success and applying the reward to its formula. This value tells the model how well it is evaluated, so it may adjust its behavior accordingly.

The proposed security model

Figure  3 illustrates the proposed ML-based security model to address IoT security issues based on NFV, SDN, and ML technologies. The figure displays the security component framework and interconnections, whereas Fig.  4 demonstrates the closed-loop automation phases, starting with detection and monitoring and ending with preventing threats. To ensure complete security, the system suggested integrating the enablers and countermeasures from the previous subsections. This framework enforces security policies beginning with the design and concluding with the application and maintenance. Two primary framework levels are shown in Fig.  3 (i.e., security orchestration and security enforcement layers). The two layers and their closed-loop automation intercommunications to detect and prevent attacks are discussed below.

The proposed ML-based security model.

Automation with a closed loop, from detection to prevention.

Security enforcement layer Several VNFs implemented on many clouds, Physical Network Functions (PNFs), and edges facilitate interaction between IoT devices and end users. These network functions (PNFs and VNFs), end users, and IoT devices interact with each other over either a conventional or an SDN-based network. The research classifies attacks on the IoT as either internal or external . The internal attack is caused by compromised and malicious IoT devices, while the external attack is initiated from the end-user network and directed at the IoT domain. The external attack creates danger for the external network and/or other authorized IoT devices. Attacks would be primarily addressed at three levels: (1) IoT devices, via IoT controllers; (2) network, via SDN controllers; and (3) cloud, via an NFV orchestrator. By implementing VNF security and setting the interaction through SDN networking, the security framework features may be properly implemented within the IoT territory. The security enforcement plan was developed to match closely with ETSI and Open Networking Foundation (ONF) guidelines for NFV and SDN. As shown in Fig.  1 , the security enforcement mechanisms consist of five separate logical blocks.

Management and control block It analyzes the components required to manage NFV and SDN infrastructures. It uses SDN controllers and ETSI MANO stack modules for this. To implement efficient security functions, the SDN controllers and NFV orchestrator must work closely together as NFV is frequently used alongside SDN to alter programmatically the network based on policies and resources.

VNF block Taking into consideration the VNFs that have been implemented across the virtualization infrastructure to implement various network-based security measures, the threat and protection measures required by the rules of security will be met with a focus on the delivery of sophisticated VNF security (e.g., IDS/IPS, virtual firewalls, etc.).

Infrastructure block It includes every hardware component needed to construct an IaaS layer, including computers, storage devices, networks, and the software used to run them in a virtualized environment. In addition to the elements of the network that are in charge of transmitting traffic while adhering to the regulations that have been specified by the SDN controller, a set of security probes is included in this plane to gather data for use by the monitoring services.

Monitoring agents block Its primary duty is reporting network activity and IoT actions to identify and prevent various types of attacks. In the proposed model, the detection technique may make use of either network patterns or IoT misbehavior. Using SDN-enabled traffic mirroring, every bit of data that is being sent over the network can be seen. The Security Orchestration Plane hosts an AI-based response agent that receives logs from the monitoring agents describing malicious transactions.

The IoT domain block It refers to the interconnected system of cameras, sensors, appliances, and other physical objects that form the SDN. The proposed methodology considers the substantial risk these devices pose to data privacy and integrity, and it tries to enforce the security standards in this domain.

Security orchestration layer This layer has the task of setting up real-time rules of security depending on the current state of monitoring data and adjusting the policies dynamically based on their context. It is a novel part of the proposed framework that communicates with the security enforcement layer to request the necessary actions to be taken to enforce security regulations inside the IoT domain. Virtual security enablers must be created, configured, and monitored to deal with the present attack.

Figure 2 is a diagrammatic representation of the major cooperation that happens among various framework components. This study proposes a feedback automation mechanism control system consisting of an oversight agent, an AI-based reaction agent, and an orchestrator for security. The latter protects against dangers by utilizing an NFV orchestrator, SDN controller, and IoT controller (see Figs. 3 , 4 ).

AI-based reaction agent This part orders the security orchestrator to perform predetermined measures in response to an incident. This block, as shown in Fig.  4 , makes use of the information collected by the monitoring agent from IoT domains and the network. This part employs ML models that have been trained on network topologies and the actions of IoT devices to identify potential dangers. For the security orchestrator, these ML models will be able to prescribe the optimal template for policies of security. Figure  4 also shows how to identify security threats from observations of network patterns and/or IoT activities. The security orchestrator would then be informed of the discovered danger level (where every level from L1 to L5 belongs to a different predefined security policy). As shown in Fig.  4 , we developed an AI-based reaction agent that uses seven ML techniques to recognize IoT-related attack activities and/or patterns in a network. These techniques are Random Forest, Decision Tree, Naive Bayes, Backpropagation NN, XGBoost, AdaBoost, and Ensemble RF-BPNN.

Security orchestrator This part of closed-loop automation enforces the AI reaction agent’s security practices. It enforces IoT security regulations utilizing SDN and NFV with the control and management block. The security orchestrator instantiates, configures, and monitors virtual security devices, manipulates bad traffic through SDN, or directly controls IoT machines, like shutting off a hacked device.

We have addressed the IoT security threats using RF, NB, DT, NNs, XGBoost, AdaBoost, and Ensemble RF-BPNN, which involve leveraging ML algorithms to detect and mitigate potential risks. To highlight their effectiveness, we can compare some of these approaches to traditional security methods as follows:

RFs are an ensemble learning algorithm that combines multiple DTs to enhance accuracy and robustness. They applied to the proposed IoT security system as follows:

Ensemble construction RF consists of multiple DTs, each trained on a randomly selected subset of the training dataset. This randomness helps to reduce overfitting and increase generalization.

Classification When classifying new instances, each DT in the RF independently predicts the class. The last prediction depends on the majority vote or averaging of the individual tree predictions.

Decision trees (DTs) are a popular ML technique for classification and regression tasks. The proposed IoT security system uses a DT classifier to identify and address unique threats, and it works as follows:

Feature selection The first stage is to select relevant features from the IoT device data. These features can include network traffic patterns, device behavior, communication protocols, and more.

Training Using a labeled dataset, we train a DT classifier that contains instances of both normal and malicious behavior. The model learns to classify instances based on the selected features.

Detection Once trained, the DT can classify new instances as normal or malicious, depending on their feature values. If the DT classified an instance as malicious, it would take appropriate security measures, such as blocking network access or raising an alarm.

Neural networks NNs, particularly DL architectures, have gained significant popularity in various domains, including IoT security. Here’s how they can be used:

Multiple layers of interconnected nodes (neurons) form the architecture design of a neural network model. Each neuron applies a non-linear activation function to weighted inputs from the previous layer.

We train the neural network using a labeled dataset through a process known as backpropagation. To reduce the discrepancy between the expected and observed labels, we iteratively tweak the network’s biases and weights.

Prediction: Once trained, the neural network can classify new instances into different threat categories based on their input features.

Comparative analysis with traditional approaches Compared to traditional security approaches, such as rule-based systems or signature-based detection, ML techniques offer several advantages. Traditional methods rely on predefined rules or patterns, which might not be able to adapt to rapidly evolving threats. In contrast, ML methods can learn from data and adapt their behavior accordingly. They can detect anomalies, identify new attack patterns, and improve over time as they encounter new threats. However, traditional approaches often provide better interpretability and explainability.

Rule-based systems explicitly define security rules, making it easier for security analysts to understand and verify their behavior. However, ML models, especially complicated ones like neural networks, are black boxes, making their decision-making process difficult to comprehend.

In conclusion, ML techniques like DTs, RFs, XGBoost, AdaBoost, and neural networks provide powerful tools for addressing unique IoT security threats. They offer improved accuracy, adaptability, and the ability to handle complex and evolving attack patterns. However, they may trade off some interpretability compared to traditional security approaches. The approach is selected based on the specific requirements of the IoT security system and the trade-offs between accuracy, interpretability, and computational requirements.

Performance evaluation of the proposed model

The experimental methodology and analysis outcomes of the AI-based response agent are covered in this section. An AI-based response agent can identify potential threats by performing the following steps: (1) Evaluate network patterns. To identify various forms of network infiltration, the research presents a knowledge-based intrusion detection framework. (2) Examine the strange behaviors that have been seen in the IoT system. Here, attacks are uncovered through the investigation of strange actions taken by IoT devices. To appropriately categorize the degree of the attacks and select the right security solutions, the research has applied supervised learning algorithms. The AI-based reaction agent will employ many ML approaches, considering the appropriate inputs from the monitoring agents, to remove a specific attack.

Evaluating network patterns Intrusion system evaluation is the first stage in evaluating the framework’s effectiveness.

Several publicly available datasets, including the UNSW_NB15, IoT-23, DARPA, KDD 99, NSL-KDD, DEFCON, and balanced BoTNeT-IoT-L01 datasets, were used to build the proposed system (see the datasets link ( https://drive.google.com/drive/folders/1gjP-pQzFZsLh2QMsIa5GPhEh5etv9Jvc?usp=sharing )). These datasets contain information on IoT attacks in the form of (.csv) files. Table 1 shows the network traffic information from different IoT devices. Advantages of the NSL-KDD dataset compared with the initial KDD dataset: The train set does not contain duplicated data; therefore, classifiers are not biased toward more frequent records. BoTNeT-IoT-L01 is a recent dataset that consists of two Botnet assaults (Gafgyt and Mirai). Over a 10-s frame with a decay factor of (0.1), the mean, count, variance, radius, magnitude, correlation coefficient, and covariance were the seven statistical measures that were computed. The .csv file was used to extract four features: jitter, packet count, outbound packet size alone, and combined outbound and inbound packet size 37 . By computing three or more statistical measures for each of the four traits, a total of twenty-three features were obtained.

Furthermore, this study used the widely recognized NSL-KDD dataset as a benchmark. It served as a benchmark for assessing intrusion detection systems in this research. It is a much better version of dataset KDD 99 (see Table 2 ). The NSL-KDD dataset has over 21 distinct attack types, which serve as the foundation for the application of our proposed IDS model, such as teardrop, satan, rootkit, buffer-overflow, smurf DDoS, pod-dos, and Neptune-dos. The NSL-KDD dataset is primarily composed of preprocessed network traffic data. These data provide a more precise representation of the network traffic that occurs at present. There are two distinct collections of data inside the dataset: a set for testing and a set for training . Comparatively, the set of testing has around 23,000 records, whereas the training set contains approximately 125,000 records. Each entry in the dataset corresponds to a network connection and contains a set of 41 features, including the IP addresses of the source and destination, protocols, flags, and a label indicating whether the connection is normal or abnormal (anomalous). Each sample in the dataset corresponds to certain attacks as follows: DoS attacks, remote-to-local (R2L) attacks, user-to-root (U2R) attacks, and probing attacks 38 . There are many implementation tools available for analyzing IoT attack datasets, such as Wireshark, Snort, Zeek (formerly Bro), Jupyter Notebook, Python, and Weka. In this work, the researchers used Python programming and Weka data mining tools for ML and data analysis processing.

The proposed tools include a large collection of ML algorithms for classification, regression, clustering, and association rule mining, such as RF, NB, DT, NNs, XGBoost, AdaBoost, and Ensemble RF-BPNN, as well as tools for model evaluation and selection, including cross-validation and ROC analysis.

Certain ML algorithms are incapable of learning due to the wide range of features present in nature. The modeling process becomes more challenging when a feature is continuous. Hence, before constructing classification patterns, preprocessing is fundamental to optimize prediction accuracy. Specifically, a discretization technique is used to overcome this restriction. When applied to a continuous variable, the discretization data mining approach seeks to minimize the number of possible values by categorizing them into intervals. Two different kinds of discretization are discussed in the literature: (1) static variable discretization , in which variables are partitioned separately, and (2) dynamic variable discretization, in which all features are discretized concurrently 39 . The research discretized the attacks and then categorized them such that the research was left with only the most common types (UDP, Junk, Ack, and UDP plain from the balanced BoTNet-IoT-L01 dataset and DDoS, Probe, U2R, and R2L from NSL-KDD).

Metrics for comparing performance Choosing measures that can indicate the strength of an IDS is a major problem when evaluating an IDS. An IDS’s performance goes well beyond its classification results alone. Cost Per Example (CPE), precision, detection rate, and model accuracy are utilized to evaluate the effectiveness of the proposed system. When evaluating outcomes, the following metrics should be used in conjunction with one another 40 .

Equation ( 1 ) indicates Cost-Sensitive Classification (CSC) or CPE, where N is the total number of samples, CM refers to the classification’s Confusion Matrix algorithm, and C is the Cost Matrix (see Table 3 ) 41 .

Input data cleaning, feature extraction, and classification The research proposes a first method, which involves preparing the entire dataset and then categorizing it using a variety of techniques (Hoeffding Tree, RF, Bayes Net, and J48) as shown in Fig.  6 . Next, the research chooses the best classifier (algorithm) that generates a preferred accuracy (see Table 4 for the BoTNet-IoT-L01 dataset and Table 5 for the NSL-KDD dataset).

Backpropagation approach To investigate the multilayer neural net approach, the research utilized the capabilities of a backpropagation technique for learning. The research employed a multilayer neural network with three layers. The initial layer had 41 inputs, representing the features of the dataset. The final layer encompassed the classification responses, namely, U2L, U2R, Probe, DoS, and Normal. An extra hidden layer was incorporated to facilitate the learning process. This method uses 100 neurons and a single hidden layer. Experience has shown that the alternative hidden layer and neuron counts did not increase the mean squared error (MSE) (see Table 6 ).

Distributed classification module This module introduces a distributed categorization system in which the various types of attacks (DDoS, U2R, R2L, and Probe; UDP, UDP plain, Ack, and Junk) are all assigned to the Ensembled RF-BPNN algorithm. Finally, the AdaBoost method is used to combine the resulting models (see Table 7 ).

Result evaluation and discussion

The findings reported in Table 5 demonstrate both the accuracy rate and precision of the RF technique. Unfortunately, the results are not promising for either U2R or U2L attacks. There is a low misclassification rate (or CPE) and high accuracy when using J48 to identify attacks. When it comes to the accuracy required for U2R strikes, however, J48 falls short. Despite its consistent performance, the Hoeffding tree method has a low accuracy for U2R threats. Although it has a strong model accuracy, the Bayes Net method provides the lowest results, failing to identify the vast majority of U2R threats. As can be seen from Table 6 , the backpropagation process is generally as precise as its predecessors, if not somewhat more so. However, misclassification comes with a significant processing time penalty. AdaBoost, CPE, and detection rate produced a better detection accuracy model as shown in Table 7 .

The performance of ML algorithms used in the proposed system

A classification algorithm for IoT detection based on ensembles of backpropagation neural networks is trained on the BoTNet-IoT-L01 dataset (see Table 8 ). The novelty of the algorithm stems from the methodology employed for combining outputs of the backpropagation neural network ensembles. The backpropagation neural network Oracle 8i database tool is utilized to combine the ensemble outputs. As Fig.  5 shows, the neural network backpropagation Oracle is constructed with an RF algorithm that produces high classification accuracy and low classification error (see Table 4 ). The thresholds are not learned all at once in the RF model but rather hierarchically. The decrease in impurity will be enforced one directionally from the starting to the finishing index of the symbolic path; however, the research learned them simultaneously. The idea of hierarchical node splits will be represented by this one-directional impurity reduction. To do this, firstly, the research breaks up each node in the symbolic path into some votes for each class. Secondly, the research computes the impurity based on those votes. The third step is to gradually lower it by a certain amount using the Softmax activation method. Our proposed algorithm uses margin ranking loss as its objective function. It is important to maintain a minimum margin disparity between the intended result and the actual one. The margin difference is the ‘reduction in impurity’. The target is output shifted by one index to the right and the impurity at first split is initialized by the impurity of the batch (see Fig.  5 ).

Architectural flow graph of the proposed RF with backpropagation NN (RF-BPNN).

When employing the AdaBoost classifier as a detection model, the research was limited to considering a single window size. Therefore, the research has successfully decreased the number of attributes in the BoTNeT-IoT-L01 dataset from 115 to 23. This significant decrease in the dimensionality of the dataset results in a significant acceleration of the detection process. Speaking of the BotNet-IoT dataset, the research discovered that just a small number of parameters have an important role in our system’s overall performance, and time windows of 10 s performed marginally better than those of shorter duration (see Fig.  6 ). Additionally, the research discovered that traffic heterogeneity greatly impacted RF classifier performance. However, when compared to the other classification algorithms, AdaBoost and RF-BPNN had the greatest and most stable results (see Table 7 ).

RF-BPNN accuracy evaluation for each attack type in the balanced BoTNet-IoT-L01 dataset.

Figure  7 shows the accuracy for detecting DoS , Fuzzers , Gene ric, Backdoor, and Exploit attacks in the UNSW_NB15 dataset using the RF classifier and SMOTE (where “ label” refers to the target variable and “attack_cat ” refers to the attack types).

The accuracy for detecting some attacks in the UNSW_NB15 dataset, using RF Classifier.

Different experiments determine the system’s performance. Examining and validating each stage using the supplied classifiers is necessary to confirm the experimental results. Whether the classifier can discriminate across feature categories is also crucial. Accuracy, specificity, precision, recall, F1-score, and AUC measure the model’s performance and indicate the correctness of the system. Such measurements are based on the T P , F P , T N , and F N , as shown in Eqs. ( 2 ) to ( 6 ):

We use the following terms to describe the classification errors: true positive (TP) for attack instances, true negative (TN) for normal cases, false positive (FP) for incorrectly classified normal instances, and false negative (FN) for incorrectly classified attack instances.

Thus, the accuracy formula evaluates the classifier’s capacity to accurately categorize both positive and negative instances; precision denotes the classifier’s ability to avoid incorrectly labeling positive instances as negative, and specificity denotes its capacity to avoid incorrectly labeling negative instances as positive. In machine learning, recall is the rate at which a classifier can identify positive examples, whereas the F1-score is the weighted average of accuracy and recall.

Table 9 shows the performance of seven machine learning classifiers using the Synthetic Minority Oversampling Technique (SMOTE) on the UNSW_NB15 dataset. As you can see in Fig.  8 , the RF, XGBoost, AdaBoost, and Ensembled RF-BPNN classifiers did the best overall. They achieved an accuracy of 99.9%, an AUC of 1, and an F1 score of 99.9%. The Naive Bayes classifier, on the other hand, obtained the minimum accuracy and F1 score.

The accuracy of 7 ML algorithms using the UNSW-NB15 dataset and SMOTE.

Integration with existing IoT security frameworks and standards

The proposed model can integrate with existing IoT security frameworks and standards as follows:

Integration with IoT security frameworks The ML-based model can integrate with IoT security frameworks by aligning its functionalities with their security objectives and guidelines. For example:

The proposed model can integrate with existing authentication mechanisms recommended by IoT security frameworks, such as digital certificates or secure bootstrapping protocols. It can enhance device authentication by analyzing device behavior patterns and detecting anomalies that may indicate unauthorized access or compromised devices.

To align with data privacy requirements, the model can utilize encryption techniques and privacy-preserving algorithms recommended by the IoT security frameworks. It provides a guarantee of secure transmission and storage of data, protecting confidential information against illegal access.

The proposed model can integrate with existing access control mechanisms defined by IoT security frameworks. It can augment access control by providing intelligent decision-making capabilities based on historical data, user behavior analysis, or contextual information. This aids in assessing access requests and preventing unauthorized access to IoT resources.

Integration with IoT security standards The ML-based model can comply with IoT security standards by incorporating the required security controls and practices. For example:

The proposed model can align with ISO/IEC 27000 standards by implementing appropriate security controls for risk assessment, incident management, and data protection. It can follow the standards’ guidelines to ensure that the necessary security measures are in place.

The model can follow the NIST framework to enhance its threat detection and incident response capabilities.

Interoperability in IoT ecosystems By adhering to standard IoT protocols, data formats, and metadata standards, the ML-based model can ensure interoperability. For example:

The ML model can communicate with IoT devices and gateways using standard IoT protocols such as MQTT or CoAP, ensuring compatibility and interoperability across different devices and platforms.

The ML model can use commonly used data formats, such as JSON, or semantic data models, such as the Semantic Sensor Network (SSN) ontology, to facilitate seamless data sharing and interoperability with other components within the IoT ecosystem.

By integrating with existing IoT security frameworks and standards, the proposed model can enhance its adaptability and compatibility within IoT ecosystems. This integration allows the model to complement and enhance the existing security infrastructure, contributing to improved IoT security outcomes.

Comparisons with related systems

Table 10 highlights the proposed model’s performance outcomes by comparing it to previous systems. This study looked at existing literature and compared it to others based on standards, like the false positive rate (FPR), CPE, accuracy, and detection rate 38 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 47 . Through several experiments, the proposed system achieved the best evaluation metrics for accuracy, precision, detection rate, CPE, and lowest time complexity compared with previous solutions, as shown in Tables 10 and 11 .

Privacy concerns and data bias

The authors of this work have incorporated essential steps into the development and deployment of the proposed ML-based security model to effectively address privacy concerns and data bias, as well as ensure the technology’s ethical and responsible use within the IoT system.

The authors conducted a privacy impact assessment to determine if the proposed ML-based security model has any privacy issues or concerns.

To mitigate privacy concerns, the study implemented privacy-enhancing techniques . This process included data anonymization, encryption, differential privacy, or federated learning, which allows for training the proposed ML model without sharing raw data.

The study minimized the amount of personally identifiable information (PII) gathered and stored to reduce privacy risks. During the requirements engineering phase, we only collected the necessary data for the proposed machine learning-based security model, ensuring its safe storage and disposal when no longer required.

We implemented regular monitoring of the proposed ML model for potential biases in data and outcomes. Implementing a bias detection process is critical for identifying discriminatory patterns. We can take steps to mitigate detected biases , which may include adjusting training data, diversifying datasets, or utilizing bias correction algorithms.

Regularly monitor the proposed ML-based security model performance, including privacy aspects, and update it as needed to address emerging privacy concerns, mitigate biases, and ensure ongoing compliance with ethical standards.

Conclusions and upcoming work

This research introduces a new proposed ML-based security model to address the vulnerabilities in IoT systems. We designed the proposed model to autonomously handle the growing number of security problems associated with the IoT domain. This study analyzed the state-of-the-art security measures, intelligent solutions, and vulnerabilities in smart systems built on the IoT that make use of ML as a key technology for improving IoT security. The study illustrated the benefits and limitations of applying ML in an IoT environment and proposed a security model based on ML that can automatically address the rising concerns about high security in the IoT domain. The suggested method performs better in terms of accuracy and execution time than existing ML algorithms, which makes it a viable option for improving the security of IoT systems. This research evaluates the intrusion detection system using the BoTNet-IoT-L01 dataset. The research applied our proposed IDS model to a dataset that included more than 23 types of attacks. This study also utilized the NSL-KDD dataset to evaluate the intrusion detection mechanism and evaluated the proposed model in a real-world smart building environment. The presented ML-based model is found to have a good accuracy rate of 99.9% compared with previous research for improving IoT systems’ security. This paper’s contribution is the development of a novel ML-based security model that can improve the efficiency of cybersecurity systems and IoT infrastructure. The proposed model can keep threat knowledge databases up to date, analyze network traffic, and protect IoT systems from newly detected attacks by drawing on prior knowledge of cyber threats. This study presents a promising ML-based security approach to enhance IoT system security. However, future work and improvements remain possible. Expanding the dataset for the intrusion detection system evaluation could be one area of improvement. While the BoTNet-IoT-L01 and NSL-KDD datasets used in this study are comprehensive, they may not cover all possible types of attacks that could occur in an IoT environment. Therefore, our future research could focus on collecting and analyzing more diverse datasets to increase the performance of the proposed model. Furthermore, optimizing the proposed model’s execution time is crucial for real-world applications. Also, we could integrate the proposed model with other security solutions to create a more comprehensive and robust security system for IoT devices. Overall, the development of this novel ML-based security model is a significant contribution to the literature on ML security models and IoT security, and further work and improvements will continue to advance the field. Finally, the security analyst treats the AI-based IDS as a black box due to its inability to explain the decision-making process 48 . In our future work, we will expand our research by integrating blockchain-based AKA mechanisms with explainable artificial intelligence (XAI) to secure smart city-based consumer applications 49 . On the other hand, we can use the Shapley Additive Explanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision 50 .

Data availability

The corresponding author can provide the datasets used and/or analyzed in this work upon reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under Grant Number (RGP1/129/45).

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Hosam El-Sofany is responsible for developing the original research concept, design, methodology, and implementation. He is also responsible for writing, editing, reviewing, checking against plagiarism using the iThenticate program, and proofreading. Samir A. El-Seoud: methodology, writing, and proofreading. Omar H. Karam: methodology, writing, and proofreading. Belgacem Bouallegue: methodology, writing, reviewing, editing, and proofreading.

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El-Sofany, H., El-Seoud, S.A., Karam, O.H. et al. Using machine learning algorithms to enhance IoT system security. Sci Rep 14 , 12077 (2024). https://doi.org/10.1038/s41598-024-62861-y

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DOI : https://doi.org/10.1038/s41598-024-62861-y

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