TITLE:
Tanimoto Based Similarity Measure for Intrusion Detection System
AUTHORS:
Alok Sharma, Sunil Pranit Lal
KEYWORDS:
Intrusion Detection, ，kNN Classifier, Similarity Measure, Anomaly Detection, Tanimoto
Similarity Measure
JOURNAL NAME:
Journal of Information Security,
Vol.2 No.4,
October
24,
2011
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.