Research of Outlier Mining Based Adaptive Intrusion Detection Techniques
Fang Yu Ke, Yan Fu, Zhou Jun Lin · 2010
The traditional IDS can not effectively manage the new continuously changing intrusion detection attacks. To deal with the problem, data mining based intrusion detection methods have been the hot fields in intrusion detection research. An outlier mining based adaptive intrusion detection framework is proposed in this paper. In the proposed framework, the outliers are firstly detected by similarity coefficient. And then, the clusters are built on the detected outlier data set and the improved association rule algorithm is employed on the clusters. Finally, the rules generated by association rule algorithm will be adaptively added into the current intrusion detection rule base. The experiments performed on simulated data and KDD99 from UCI data set have shown the effectiveness of proposed methods.