Evolving discrete-valued anomaly detectors for a network intrusion detection system using negative selection


Powers, Simon T. and He, Jun (2006) Evolving discrete-valued anomaly detectors for a network intrusion detection system using negative selection. In, the 6th UK Workshop on Computational Intelligence (UKCI'06), University of Leeds, , 41-48.

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Description/Abstract

Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. One approach is anomaly detection, where deviations from a model of normal network activity are reported. The negative selection algorithm, inspired by the immune system, can be used to generate anomaly detectors. Previous work has applied a genetic algorithm to generate real-valued detectors. However, we argue that at least some discrete fields are required in detectors, e.g. the port number. The system reported in this paper evolves discrete-valued detectors, which we show are able to outperform real-valued detectors.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: 04/09/2006
Related URLs:
Keywords: artificial immune system; intrusion detection; negative selection
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 264052
Date Deposited: 23 May 2007
Last Modified: 27 Mar 2014 20:08
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/264052

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