Intrusion Detection systems (IDS) play an important role in network security and protection. Intrusion detection system uses either misuse or anomaly based techniques to identify malicious activities. To detect malicious activity, misuse detection systems is used to identify signatures or previously known malicious activities. On the other hand, anomaly based systems is used to identify unknown attacks. Intrusion detection system is now an essential tool to protect the networks by monitoring inbound and outbound activities and identifying suspicious patterns that may indicate a system attack. In recent years, some researchers have employed data mining techniques for developing IDS. In this paper, hybrid Particle Swarm Optimization (PSO) and Fuzzy c-means clustering for network Intrusion Detection is proposed to identify intrusion over NSL-KDD dataset. An attempt has been made to cluster the dataset into normal and the major attack categories i.e. DoS, R2L, U2R and Probe. The experimental results demonstrate the efficiency of the proposed approach.

IDS, Fuzzy c-means Algorithm, PSO, Mutual Information, NSL-KDD Dataset

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