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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 16:05
    Last Modified: 18 Jul 2012 14:01
    URI: http://repository.usp.ac.fj/id/eprint/4815
    UNSPECIFIED

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