Machine Learning for Cybersecurity: A Comprehensive Literature Review

In order to gain an overview of the state of research using machine learning for applications in cybersecurity, we have carried out a quantitative analysis of published works using the Scopus database. In total, \(81{,}082\) articles in this area were scraped from Scopus, from which we created an article-citation graph with articles as nodes and \(547{,}998\) citations among them as edges. We used the Louvain method for community-finding to organize the articles into research areas based on citation patterns, resulting in 12 identified research areas: intrusion detection: classical machine learning, intrusion detection: deep learning, privacy-preserving machine learning, malware detection, biometrics, adversarial machine learning, steganalysis, neural cryptography, phishing, traffic classification, software vulnerabilities, and smart grid. The state of the whole field and of each research area is discussed in detail, including highly cited and seminal works in each area and the relative size of each area compared to each other. Compared to previous literature surveys on this topic this review is not limited to specific subtopics and organizes the field by grouping works based on research topics, giving a more comprehensive overview of the field and the areas it encompasses.