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The network intrusion detection systems which based on anomaly detection techniques plays an important role in protection network and systems from harmful attacks. With increasing in attacks and the new security challenges, The lower accuracy of anomaly detection method based on cluster analysis network traffic is a big question, In this paper, we proposed a hybrid anomaly detection method by combining the Particle Swarm Optimization(PSO) and K-Means clustering algorithms improving the accuracy. We first preprocess features of data traffic, extract the characteristics of the various categories of attack, and then use parallel PSO calculation, to find the best or a little approximations to optimal clustering initial point. Finally, we perform the K-Means clustering algorithm. Experiment results show the effectiveness of the proposed optimization scheme.

A hybrid approach for anomaly detection using K-means and PSO