Feature Grouping for Intrusion Detection System Based on Hierarchical Clustering

Abstract : Intrusion detection is very important to solve an increasing number of security threats. With new types of attack appearing continually, traditional approaches for detecting hazardous contents are facing a severe challenge. In this work, a new feature grouping method is proposed to select features for intrusion detection. The method is based on agglomerative hierarchical clustering method and is tested against KDD CUP 99 dataset. Agglomerative hierarchical clustering method is used to construct a hierarchical tree and it is combined with mutual information theory. Groups are created from the hierarchical tree by a given number. The largest mutual information between each feature and a class label within a certain group is then selected. The performance evaluation results show that better classification performance can be attained from such selected features.
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Jingping Song, Zhiliang Zhu, Chris Price. Feature Grouping for Intrusion Detection System Based on Hierarchical Clustering. International Cross-Domain Conference and Workshop on Availability, Reliability, and Security (CD-ARES), Sep 2014, Fribourg, Switzerland. pp.270-280, ⟨10.1007/978-3-319-10975-6_21⟩. ⟨hal-01404004⟩

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