Tanimoto based similarity measure for intrusion detection system

Sharma, Alokanand and Lal, Sunil P. (2011) Tanimoto based similarity measure for intrusion detection system. Journal of Information Security, 2 (4). pp. 195-201. ISSN 2153-1234

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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.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Ms Shalni Sanjana
Date Deposited:	25 Jun 2011 04:05
Last Modified:	18 Jul 2012 02:01
URI:	https://repository.usp.ac.fj/id/eprint/4815

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