Research and Application of an improved Support Vector Clustering Algorithm on Anomaly Detection
Sheng Sun, Yuanzhen Wang · Journal of Software · 2010
Anomaly detection approaches build models of normal data and detect deviations from the normal model in observed data. Anomaly detection applied to intrusion detection and computer security has been an active area of research. The major benefit of anomaly detection algorithms is their ability to potentially detect unforeseen attacks. In this paper, a novel weighted support vector clustering algorithm for anomaly detection is proposed. 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.