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Abstract
With the expanding and increasing use of networks and accumulating number of inter- net users, network throughput has become massive and threats are more diverse and sophisticated. Network and information security are of high importance, and research is continuous in these {uFB01}elds to keep up with the increasing complexity of attacks. Intrusion Detection is a major research area that aims to identify suspicious activities in a moni- tored system, from authorized and unauthorized users, by monitoring and analyzing the system activities. In this thesis, a multi-agent network intrusion detection system is implemented, inspired by a biological immunity technique called the Negative selection Approach. The system detects network tra{uFB03}c anomalies using detectors generated by a genetic algorithm with deterministic crowding Niching technique. As it is inspired by the negative selection mechanism of the immune system, it can detect foreign patterns in the complement (non-self) space