Clustering based on swarm intelligence with application to anomaly intrusion detection

A clustering algorithm based on swarm intelligence is systematically proposed for anomaly intrusion detection. The basic idea of our approach is to produce the cluster by swarm intelligence-based clustering. Instead of using the linear segmentation function of the CSI model, here we propose to use a nonlinear probability conversion function and can help to solve linearly inseparable problems. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. And then the identified cluster can be used in real data detection. In the traditional clustering-based intrusion detection algorithms, clustering using a simple distance-based metric and detection based on the centers of clusters, which generally degrade detection accuracy and efficiency. Our approach can settle these problems effectively. The experiment result shows that out approach can detect unknown intrusions efficiently in the real network connections.