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.
Tanimoto based a similarity measure for hostbased intrusions on a binary feature set for training and classification introduced by REF .
Tanimoto Based Similarity Measure for Intrusion Detection System
J. Information Security
J. Information Security
Computer Science