Clustering-Based and Supervised Intrusion Detection Method
Shengyi Jiang, Qinghuai Li · Mini-micro Systems · 2005
A clustering-based and supervised intrusion detection method,named CBSID(Clustering-based and Supervised Interusion Detection) with new distance defination is proposed in this paper. CBSID clusters training data by the label and the results of clustering are used as classification model to predict which cluster the current data belongs to.The method is robust to the cluster parameter and the input sequence of data. The classifiaction model may be incremental updated.Compared with the most existing supervised intrusion detection methods, this method can detect unknown intrusions.The experiment results on dataset KDDCUP99 demonstrate that the CBSID has promising performance with high detection rate and low false alarm rate.