Abstract

In cyber attribution, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. In this chapter, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is especially suitable for handling such contradictory and uncertain data–hence the framework would be well-suited for cyber attribution. We conclude with the demonstration—via a case study—of how our framework can be used to address the attribution problem in cybersecurity.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages17-45
Number of pages29
Edition9783319737874
DOIs
StatePublished - Jan 1 2018

Publication series

NameSpringerBriefs in Computer Science
Number9783319737874
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Nunes, E., Shakarian, P., Simari, G. I., & Ruef, A. (2018). Argumentation-based cyber attribution: The DeLP3E model. In SpringerBriefs in Computer Science (9783319737874 ed., pp. 17-45). (SpringerBriefs in Computer Science; No. 9783319737874). Springer. https://doi.org/10.1007/978-3-319-73788-1_3