Applying Argumentation Models for Cyber Attribution

A major challenge in cyberthreat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. In this chapter, we leverage the dataset from the capture-the-flag event held at DEFCON discussed in Chap. 2, and propose DeLP3E model comprised solely of the AM (that is, without probabilistic information) designed to aid an analyst in attributing a cyberattack. We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the accuracy of the classification-based approaches discussed in Chap. 2 from 37% to 62% in identifying the attacker.