A systematic review of defensive and offensive cybersecurity with machine learning

Imatitikua D. Aiyanyo, Hamman Samuel, Heuiseok Lim

Research output: Contribution to journalReview articlepeer-review

18 Citations (Scopus)


This is a systematic review of over one hundred research papers about machine learning methods applied to defensive and offensive cybersecurity. In contrast to previous reviews, which focused on several fragments of research topics in this area, this paper systematically and comprehensively combines domain knowledge into a single review. Ultimately, this paper seeks to provide a base for researchers that wish to delve into the field of machine learning for cybersecurity. Our findings identify the frequently used machine learning methods within supervised, unsupervised, and semi-supervised machine learning, the most useful data sets for evaluating intrusion detection methods within supervised learning, and methods from machine learning that have shown promise in tackling various threats in defensive and offensive cybersecurity.

Original languageEnglish
Article number5811
JournalApplied Sciences (Switzerland)
Issue number17
Publication statusPublished - 2020 Sept

Bibliographical note

Publisher Copyright:
© 2020 by the authors.


  • Artificial intelligence
  • Cybersecurity
  • Data mining
  • Defensive security
  • Intrusion detection systems
  • Machine learning
  • Offensive security

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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