Tanimoto Based Similarity Measure for Intrusion Detection System

Alok Sharma, Sunil Pranit Lal
DOI: 10.4236/jis.2011.24019                

Abstract

In this paper we introduced Tanimoto based similarity measure for host-based intrusions using binary feature set for training and classification. The k-nearest neighbor (kNN) classifier has been utilized to classify a given process as either normal or attack. The experimentation is conducted on DARPA-1998 database for intrusion detection and compared with other existing techniques. The introduced similarity measure shows promising results by achieving less false positive rate at 100% detection rate.

Keywords

Intrusion Detection, ，kNN Classifier, Similarity Measure, Anomaly Detection, Tanimoto Similarity Measure

Conflicts of Interest

The authors declare no conflicts of interest.

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