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http://dx.doi.org/10.14400/JDC.2018.16.5.399

A Study on Intrusion Detection in Network Intrusion Detection System using SVM  

YANG, Eun-mok (School of Software, Soongsil University)
Seo, Chang-Ho (Dept. of Applied Mathematics, Kongju National University)
Publication Information
Journal of Digital Convergence / v.16, no.5, 2018 , pp. 399-406 More about this Journal
Abstract
Much research has been done using the KDDCup99 data set to study intrusion detection using artificial intelligence. Previous studies have shown that the performance of the SMO (SVM) algorithm is superior. However, intrusion detection studies of new intrusion types not used in training are insufficient. In this paper, a model was created using the instances of weka's SMO and KDDCup99 training data set, kddcup.data.gz. We tested existing instances(292,300) of the corrected.gz file and new intrusions(18,729). In general, intrusion labels not used in training are not tested, so new intrusion labels were changed to normal. Of the 18,729 new intrusions, 1,827 were classified as intrusions. 1,827 instances classified as new intrusions are buffer_overflow. Three, neptune. 392, portsweep. 164, ipsweep. 9, back. 511, imap. 1, satan. Dogs, 645, nmap. 102.
Keywords
Weka; SMO; KDDcup99; Intrusion Detection; SVM; New Intrusion Type;
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