Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review
James Halvorsen, Clemente I. Izurieta, Haipeng Cai, Assefaw Hadish Gebremedhin · ACM Computing Surveys · 2024
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 article 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.