International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Role of Machine Learning in Intrusion Detection System: A Systematic Review

  • Alhasani, Areej (College of Computer and Information Technology, University of Tabuk) ;
  • Al omrani, Faten (College of Computer and Information Technology, University of Tabuk) ;
  • Alzahrani, Taghreed (College of Computer and Information Technology, University of Tabuk) ;
  • alFahhad, Rehab (College of Computer and Information Technology, University of Tabuk) ;
  • Alotaibi, Mohamed (College of Computer and Information Technology, University of Tabuk)
  • 투고 : 2022.03.05
  • 발행 : 2022.03.30
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초록

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

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과제정보

We thank the University of Tabuk for providing research support and facilities. We would like to thank all the reference authors of whom their input made this article a reality.

참고문헌

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