A Weighted Support Vector Clustering Algorithm and its Application in Network Intrusion Detection
Sheng Sun, Yuanzhen Wang · 2009
Network Intrusion detection is an area that has received much attention in recent years. Following the anomaly detection approach, we propose a new weighted support vector clustering algorithm and apply it to the anomaly detection problem. The weight to each input point is defined according to the position of samples in sphere space. The results of experiment demonstrate that the algorithm has excellent capability and applying it in intrusion detection system can be an effective way via using the data sets of KDD cup 99.