A probabilistic analysis framework for malicious insider threats

Chen, Taolue, Kammueller, Florian ORCID logoORCID: https://orcid.org/0000-0001-5839-5488, Nemli, Ibrahim and Probst, Christian (2015) A probabilistic analysis framework for malicious insider threats. Human Aspects of Information Security, Privacy, and Trust: Third International Conference, HAS 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Proceedings. In: 3rd International Conference on Human Aspects of Information Security, Privacy and Trust, HAS 2015, held as part of HCI International 2015, 2-7 Aug 2015, Los Angeles, California, USA. ISBN 9783319203751. ISSN 0302-9743 [Conference or Workshop Item]

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Malicious insider threats are difficult to detect and to mitigate. Many approaches for explaining behaviour exist, but there is little work to relate them to formal approaches to insider threat detection. In this work we present a general formal framework to perform analysis for malicious insider threats, based on probabilistic modelling, verification, and synthesis techniques. The framework first identifies insiders' intention to perform an inside attack, using Bayesian networks, and in a second phase computes the probability of success for an inside attack by this actor, using probabilistic model checking.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Online ISBN: 978-3-319-20376-8.
Published paper appears in: Human Aspects of Information Security, Privacy, and Trust, Volume 9190 of the series Lecture Notes in Computer Science pp 178-189, 2015
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 15190
Notes on copyright: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-20376-8_16
Useful Links:
Depositing User: Florian Kammueller
Date Deposited: 23 Apr 2015 10:32
Last Modified: 29 Nov 2022 22:33
URI: https://eprints.mdx.ac.uk/id/eprint/15190

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