A systematic review of defensive and offensive cybersecurity with machine learning

Imatitikua D. Aiyanyo, Hamman Samuel, Heuiseok Lim

Research output: Contribution to journalReview article

Abstract

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)
Volume10
Issue number17
DOIs
Publication statusPublished - 2020 Sep

Keywords

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

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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