An unsupervised anomaly intrusion detection algorithm based on swarm intelligence
Yong Feng, Zhongfu Wu, Kaigui Wu, Zhongyang Xiong, Ying Jie Zhou · 2005
An approach to network intrusion detection is investigated, based on swarm intelligence. The basic idea of the method is to produce the cluster by swarm intelligence-based clustering. 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 based on swarm intelligence can settle these problems effectively. The experiment result shows that our approach can detect unknown intrusions efficiently in the real network connections.