Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 12, Issue 2


Published
on

April 5, 2021


Pages

2674-2679


DOI

Article

A Comparative Analysis on Hybrid SVM for Network Intrusion Detection System


Authors

Venugopal Gaddam Affiliation:
Research Scholar, Dr. Y.S. Rajasekhar Reddy University College of Engineering & Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur
and Ramamohan Babu Gatram Affiliation:
Professor, Department of Information Technology, RVR & JC College of Engineering, Chowdavaram, Guntur


Abstract

Rapid growth in technology, not only makes smoother the life style, but also reveals a lot of security issues. Day by day changing of attack types distracts not only organizations, companies but also the people who are using network services for their daily needs. Intrusion Detection Systems (IDS) have been developed to avoid financial losses caused by network attacks. KDD CUP 99, NSL-KDD, KYOTO 2006+, CIDDS-01 etc., some of the Intrusion Datasets available for researchers to test and develop their IDS models. In this paper, an attempt is made to compare the effect of various SVM Kernel based models and Hybrid kernel based models etc., on CIDDS-01 dataset. Results were drawn.


Keywords

IDS, Machine Learning, Support Vector Model, Kyoto 2006+ dataset, CIDDS-01, Intrusion Detection System


Citation

Gaddam, V. & Gatram, R. B. (2021). A comparative analysis on hybrid SVM for network intrusion detection system. Turkish Journal of Computer and Mathematics Education, 12(2), 2674–2679.

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