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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 Wang, Xue Z. and Li, Rui Fa (eds.) At the 6th UK Workshop on Computational Intelligence (UKCI'06). , pp. 41-48.

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: 2006
Additional Information: Event Dates: 04/09/2006
Venue - Dates: the 6th UK Workshop on Computational Intelligence (UKCI'06), 2006-09-04
Keywords: artificial immune system, intrusion detection, negative selection
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 264052
URI: http://eprints.soton.ac.uk/id/eprint/264052
PURE UUID: e84b3140-ff7f-47a8-a598-580162147beb

Catalogue record

Date deposited: 23 May 2007
Last modified: 18 Jul 2017 07:40

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Contributors

Author: Simon T. Powers
Author: Jun He
Editor: Xue Z. Wang
Editor: Rui Fa Li

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