An Hybrid Intrusion Detection Approach based on SVM Classification and k-NN

Authors(2) :-A. Anbarasa Kumar, Kumar Parasuraman

Communication of information between various organizations to maintain a high level security to ensure safe and trusted communication is very important. Nowadays in internet secure data communication is not may be possible and other network also. There is thread of intrusion and misuses are occurs in any kinds of networks. We need to detect and recognize these threads and prevent cyber attacks. In this paper IDS (Intrusion Detection System) using a SVM classifier (Support Vector Machine) and to prevent the network attacks like probe attacks , DoS denial of service, R 2 L remote to user ,U 2 R user to root attacks using SSP (Sniffer and Snooping Process). Intrusion Detection has been an essential countermeasure to secure registering frameworks from noxious attacks. To enhance identification execution and decrease predisposition towards visit attacks, this paper proposes a hybrid strategy in view of SVM classification and k-NN procedure. Trial comes about show that the proposed strategy beats baselines regarding different assessment criteria. Specifically, for U2R and R2L attacks, the F1-scores of the proposed technique are substantially higher than those of baselines. Besides, comparisons with some ongoing hybrid approaches are additionally recorded. The outcomes show that the proposed strategy is focused.

Authors and Affiliations

A. Anbarasa Kumar
Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India
Kumar Parasuraman
Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India

IDS (Intrusion Detection System) , DOS Denial of service, R 2 L Remote to User ,U 2 R User to Root , Probe Attacks.

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 839-852
Manuscript Number : CSEIT1835200
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

A. Anbarasa Kumar, Kumar Parasuraman, "An Hybrid Intrusion Detection Approach based on SVM Classification and k-NN", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.839-852, May-June-2018.
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