A supervised intrusion detection method
Qinghua Li, Shengyi Jiang, Xin Li · 2005
A supervised intrusion detection method with new distance definition is proposed in this paper. This method based on constrained clustering, uses the produced clusters as classification model to predict which cluster the current data belongs to. The time complexity of the method is nearly linear with the size of dataset, the number of attributes and the final number of clusters. It is difference from existing supervised methods that our method can detect unknown intrusions. The experiment results on dataset KDDCUP99 demonstrate that the method has promising performance with high detection rate and low false alarm rate.