@inbook{4cec75f62c594ff9a23c8c4edb1f573e,
title = "Applying argumentation models for cyber attribution",
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.",
author = "Eric Nunes and Paulo Shakarian and Simari, {Gerardo I.} and Andrew Ruef",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2018. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2018",
doi = "10.1007/978-3-319-73788-1_5",
language = "English (US)",
series = "SpringerBriefs in Computer Science",
publisher = "Springer",
number = "9783319737874",
pages = "75--84",
booktitle = "SpringerBriefs in Computer Science",
edition = "9783319737874",
}