Clustering Malicious Actors: A Three Part Artificial Intelligence Story

Expert Insights

The Result of Clustering Malicious Actors Through Artificial Intelligence

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In today’s digital world, malicious actors have access to a multitude of ways to preserve and increase their anonymity online. We recently posted an article highlighting various Open-Source Intelligence (OSINT) methods to profile cybercriminals on the darkweb, and you could consider this a follow-up and in-depth dive into the Artificial Intelligence  (AI) based technologies we use to track, identify, and compare malicious actors. 

Whilst we previously covered some ways to identify the same actor across multiple platforms or various monikers, the following approach is focused on identifying similarities between actors and by doing so, revealing similar actors. Indeed, using techniques from the field of Natural Language Processing (NLP), AI allows us to easily and rapidly identify similar actors to a previously selected actor. 

By using the method of machine learning, our Data Science Team was able to dive into:

  • The wide range on benefits of identifying similar actors, including why clustering malicious actors with AI reduces organizational cyber risk
  • A deep dive into how our team was able to cluster malicious actors with Natural Language Processing (NLP), from gathering information to building a vocabulary that AI can learn from
  • The results found from the study including key examples of malicious actors clustered and how this feature is integrated into the Flare platform