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Detecting anomalous process behaviour using second generation Artificial Immune Systems

Twycross, Jamie; Aickelin, Uwe; Whitbrook, Amanda

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Authors

Uwe Aickelin

Amanda Whitbrook



Abstract

Artificial Immune Systems have been successfully applied to a number of problem domains including fault tolerance and data mining, but have been shown to scale poorly when applied to computer intrusion detection despite the fact that the biological immune system is a very effective anomaly detector. This may be because AIS algorithms have previously been based on the adaptive immune system and biologically-naive models. This paper focuses on describing and testing a more complex and biologically-authentic AIS model, inspired by the interactions between the innate and adaptive immune systems. Its performance on a realistic process anomaly detection problem is shown to be better than standard AIS methods (negative-selection), policy-based anomaly detection methods (systrace), and an alternative innate AIS approach (the DCA). In addition, it is shown that runtime information can be used in combination with system call information to enhance detection capability.

Citation

Twycross, J., Aickelin, U., & Whitbrook, A. (2010). Detecting anomalous process behaviour using second generation Artificial Immune Systems. International Journal of Unconventional Computing, 6(3-4),

Journal Article Type Article
Acceptance Date Feb 10, 2010
Publication Date Jan 1, 2010
Deposit Date Jun 16, 2016
Publicly Available Date Jun 16, 2016
Journal International Journal of Unconventional Computing
Print ISSN 1548-7199
Electronic ISSN 1548-7202
Publisher Old City Publishing
Peer Reviewed Peer Reviewed
Volume 6
Issue 3-4
Keywords Second Generation Artificial Immune Systems, Innate Immunity, Process Anomaly Detection, Intrusion Detection Systems
Public URL https://nottingham-repository.worktribe.com/output/1013383
Publisher URL http://www.oldcitypublishing.com/pdf/693
Contract Date Jun 16, 2016

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