A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning

• Main Authors: Imatitikua D. Aiyanyo, Hamman Samuel, Heuiseok Lim
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Applied Sciences
• Subjects: cybersecurity; machine learning; artificial intelligence; data mining; defensive security; offensive security
• Online Access: https://www.mdpi.com/2076-3417/10/17/5811

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.