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 and Applied Science > Electronics and Computer Science |
| Item ID: | 264052 |
| Date Deposited: | 23 May 2007 |
| Last Modified: | 02 Mar 2012 12:00 |
| Contributors: | Powers, Simon T. (Author) He, Jun (Author) Wang, Xue Z. (Editor) Li, Rui Fa (Editor) |
| Date: | 2006 |
| Additional Information: | Event Dates: 04/09/2006 |
| Status: | Published |
| Further Information: | Google Scholar |
| URI: | http://eprints.soton.ac.uk/id/eprint/264052 |
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