Nested Dirichlet models for unsupervised attack pattern detection in honeypot data

arXiv:2301.02505v2 Announce Type: replace
Abstract: Cyber-systems are under near-constant threat from intrusion attempts. Attacks types vary, but each attempt typically has a specific underlying intent, and the perpetrators are typically groups of individuals with similar objectives. Clustering attacks appearing to share a common intent is very valuable to threat-hunting experts. This article explores Dirichlet distribution topic models for clustering terminal session commands collected from honeypots, which are special network hosts designed to entice malicious attackers. The main practical implications of …

arxiv attack attacks clustering cs.cr cyber data detection honeypot hunting intent intrusion near nested objectives share stat.ap systems threat types under