Using a Stack to Find an AI Needle : Topic Modeling for Cyber Threat Intelligence

Abstract

Cyber Threat Intelligence (CTI) is a fundamental activity to ensure the protection of modern organizations against sophisticated cyberattackers. A large body of literature has addressed problems related to CTI. Despite the scientific validity of such results, the reality is that CTI practitioners rarely deploy advanced CTI methods proposed by the research community and mostly rely on manual processes. We seek to facilitate the manual analyses typical for CTI practice by proposing a novel topic modeling technique that enables analysts to identify specific topics in CTI data sources. We demonstrate how our method, released as an open source tool, can be used to investigate three case studies revolving around the research question whether attackers are deploying AI for malicious purposes “in the wild,” and, if so, what features of AI interest them the most. We analyzed 7 million discussions from 18 underground forums. Our findings reveal that attackers may favor easy-to-use AI toolkits over the sophisticated AI techniques envisioned in research papers. Our contributions are further validated by a user study (N = 24) with CTI experts, confirming the relevance of our research. Ultimately, we advocate future endeavors to account for the opinion of CTI practitioners—who should, in turn, try to cooperate.