Open AccessA Comparative Analysis on Hybrid SVM for Network Intrusion Detection System
Author(s) -
Gaddam Venugopal
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.2290
Subject(s) - intrusion detection system , computer science , support vector machine , kernel (algebra) , intrusion , data mining , network security , machine learning , artificial intelligence , computer security , mathematics , geochemistry , combinatorics , geology
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 distractsnot 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.