A Probabilistic Analysis Framework for Malicious Insider Threats

Taolue Chen; Florian Kammüller; Ibrahim Nemli; Christian W. Probst

doi:10.1007/978-3-319-20376-8_16

A Probabilistic Analysis Framework for Malicious Insider Threats

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

18 Citations (Scopus)

2 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 language	English
Title of host publication	Human Aspects of Information Security, Privacy, and Trust
Subtitle of host publication	Third International Conference, HAS 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Proceedings
Editors	Theo Tryfonas, Ioannis Askoxylakis
Place of Publication	Berlin
Publisher	Springer
Pages	178-189
Number of pages	12
ISBN (Electronic)	978-3-319-20376-8
ISBN (Print)	9783319203751
DOIs	https://doi.org/10.1007/978-3-319-20376-8_16
Publication status	Published - 21 Jul 2015
Event	Third International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS), Los Angeles, US: Third International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS) - Berlin Duration: 21 Jul 2015 → …

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	9190
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Third International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS), Los Angeles, US
City	Berlin
Period	21/07/15 → …

Keywords

EC Grant Agreement nr.: FP7/2007-2013
EC Grant Agreement nr.: FP7/318003
Malicious insider threats
Probabilistic analysis

Access to Document

10.1007/978-3-319-20376-8_16

Cite this

Chen, T., Kammüller, F., Nemli, I., & Probst, C. W. (2015). A Probabilistic Analysis Framework for Malicious Insider Threats. In T. Tryfonas, & I. Askoxylakis (Eds.), 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 (pp. 178-189). (Lecture notes in computer science; Vol. 9190). Springer. https://doi.org/10.1007/978-3-319-20376-8_16

Chen, Taolue ; Kammüller, Florian ; Nemli, Ibrahim et al. / 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. editor / Theo Tryfonas ; Ioannis Askoxylakis. Berlin : Springer, 2015. pp. 178-189 (Lecture notes in computer science).

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title = "A Probabilistic Analysis Framework for Malicious Insider Threats",

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{\textquoteright} 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.",

keywords = "EC Grant Agreement nr.: FP7/2007-2013, EC Grant Agreement nr.: FP7/318003, Malicious insider threats, Probabilistic analysis",

author = "Taolue Chen and Florian Kamm{\"u}ller and Ibrahim Nemli and Probst, \{Christian W.\}",

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language = "English",

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series = "Lecture notes in computer science",

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editor = "Theo Tryfonas and Ioannis Askoxylakis",

booktitle = "Human Aspects of Information Security, Privacy, and Trust",

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Chen, T, Kammüller, F, Nemli, I & Probst, CW 2015, A Probabilistic Analysis Framework for Malicious Insider Threats. in T Tryfonas & I Askoxylakis (eds), 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. Lecture notes in computer science, vol. 9190, Springer, Berlin, pp. 178-189, Third International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS), Los Angeles, US, Berlin, 21/07/15. https://doi.org/10.1007/978-3-319-20376-8_16

A Probabilistic Analysis Framework for Malicious Insider Threats. / Chen, Taolue; Kammüller, Florian; Nemli, Ibrahim et al.
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. ed. / Theo Tryfonas; Ioannis Askoxylakis. Berlin: Springer, 2015. p. 178-189 (Lecture notes in computer science; Vol. 9190).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Kammüller, Florian

AU - Nemli, Ibrahim

AU - Probst, Christian W.

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Y1 - 2015/7/21

N2 - 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.

AB - 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.

KW - EC Grant Agreement nr.: FP7/2007-2013

KW - EC Grant Agreement nr.: FP7/318003

KW - Malicious insider threats

KW - Probabilistic analysis

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DO - 10.1007/978-3-319-20376-8_16

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BT - Human Aspects of Information Security, Privacy, and Trust

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Chen T, Kammüller F, Nemli I, Probst CW. A Probabilistic Analysis Framework for Malicious Insider Threats. In Tryfonas T, Askoxylakis I, editors, 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. Berlin: Springer. 2015. p. 178-189. (Lecture notes in computer science). doi: 10.1007/978-3-319-20376-8_16

A Probabilistic Analysis Framework for Malicious Insider Threats

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