Threatify: APT Threat Variant Generation Using Graph-Based Machine Learning

Ensuring cybersecurity in an ever-evolving threat landscape requires proactive identification and understanding of potential threats. Conventional detection and prediction solutions often fall short as they predominantly focus on known attack vectors. Advanced Persistent Threats (APTs) are becoming increasingly sophisticated and stealthy, resulting in new threat variants that are undetectable by these detection solutions. This paper introduces Threatify, a novel approach to predicting the most probable threat variants from existing APTs and previously seen attack campaigns. Our approach automates the generation of threat variants using graph-based machine learning based on the attack definition, past attack campaigns, and the security context between different techniques. Threatify leverages a security knowledge base of realistic attack scenarios and cybersecurity expertise to model, generate, and predict new forms of potential future threats by combining inter- (i.e., within the same APT attack) and intra- (i.e., between different APTs) techniques used by threat actors. It is crucial to emphasize that Threatify does not merely mix techniques from different APTs; rather, it constructs a logical and pragmatic kill chain based on their security context. Threatify is able to predict new attack steps, find relevant techniques to be substituted by, and merge APTs’ techniques in the current security context, and thus create previously unexplored threat variants. Our extensive experimental results demonstrate the efficacy of our approach in generating relevant and novel threat variants with a similarity score of 92%, uniqueness of 82%, validity of 95%, and reduction rate of 96%, including those that have never occurred before.