A Probabilistic Analysis Framework for Malicious Insider Threats

Taolue Chen, Florian Kammuller, Ibrahim Nemli, Christian W. Probst

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

335 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the third International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS 2015)
EditorsTheo Tryfonas, Ioannis Askoxylakis
PublisherSpringer
Publication date2015
Pages178-189
ISBN (Print)978-3-319-20375-1
ISBN (Electronic)978-3-319-20376-8
DOIs
Publication statusPublished - 2015
Event3rd International Conference on Human Aspects of Information Security, Privacy and Trust (HAS 2015) - Los Angeles, United States
Duration: 2 Aug 20157 Aug 2015
Conference number: 3
http://2015.hci.international/has

Conference

Conference3rd International Conference on Human Aspects of Information Security, Privacy and Trust (HAS 2015)
Number3
CountryUnited States
CityLos Angeles
Period02/08/201507/08/2015
OtherHeld as Part of HCI International 2015
Internet address
SeriesLecture Notes in Computer Science
Volume9190
ISSN0302-9743

Fingerprint Dive into the research topics of 'A Probabilistic Analysis Framework for Malicious Insider Threats'. Together they form a unique fingerprint.

Cite this