Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review

Abstract

Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false positive rates. Generative Machine Learning Models (GMLMs) can help overcome these challenges. This paper offers an in-depth exploration of GMLMs’ application to intrusion detection. It gives: (1) a systematic mapping study of research at the intersection of GMLMs and IDSs, and (2) a detailed review providing insights and directions for future research.