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
Full text not available from this repository.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: | 26 May 2007 21:14 |
| Last Modified: | 18 Jul 2012 02:22 |
| URI: | https://repository.usp.ac.fj/id/eprint/838 |
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