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

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Title Intrusion detection using text processing techniques with a kernel based similarity measure
Author Sharma, Alokanand; Pujari, Arun Kumar; Paliwal, Kuldip Kumar
Journal Name Computers & Security
Year Published 2007
Place of publication Netherlands
Publisher Elsevier
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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/405877/description#description
Alternative URI http://dx.doi.org/10.1016/j.cose.2007.10.003
Volume 26
Page from 488
Page to 495
ISSN 0167-4048
Date Accessioned 2008-01-24
Date Available 2009-09-21T05:47:47Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/17394
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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