Title: Research of outlier detection based adaptive intrusion detection techniques
Abstract: Most traditional techniques in intrusion detection are mining the rule patterns of each attacks' features from the data we have known,then match the new data with these rules.However,the main problem of rule based intrusion detection techniques is that the current rule patterns 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 detection 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 experiment platform was based on IDS Snort and IDS Informer was employed to simulate the attack and test.The experiments performed on simulated data and KDD99 from UCI data set have shown the effectiveness of proposed methods.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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