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Intrusion detection using text processing techniques with a Kernel based similarity measure

Sharma, Alokanand and Pujari, A.K. and Paliwal, K.K. (2007) Intrusion detection using text processing techniques with a Kernel based similarity measure. Computers and Security, 26 (7 & 8). pp. 488-495. ISSN 0167-4048

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Abstract

This paper focuses on intrusion detection based on system call sequences using text processing techniques. It introduces kernel based similarity measure for the detection of host-based intrusions. The k-nearest neighbour (kNN) classifier is used to classify a process as either normal or abnormal. The proposed technique is evaluated on the DARPA-1998 database and its performance is compared with other existing techniques available in the literature. It is shown that this technique is significantly better than the other techniques in achieving lower false positive rates at 100% detection rate.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User: Ms Mereoni Camailakeba
Date Deposited: 27 May 2007 09:14
Last Modified: 18 Jul 2012 14:22
URI: http://repository.usp.ac.fj/id/eprint/838
UNSPECIFIED

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