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Erschienen in:
2018 | OriginalPaper | Buchkapitel
5. Applying Argumentation Models for Cyber Attribution
verfasst von : Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Andrew Ruef
Erschienen in: Artificial Intelligence Tools for Cyber Attribution
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Abstract
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.