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Volume & Issue no: Volume 6, Issue 3, May - June 2017

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Title:
Least Square Support Vector Machine based IDS, using feature selection algorithm
Author Name:
Rekha Preethi M.C, Mr.Chetan R
Abstract:
Abstract: When the different users on the Internet access similar content which may be redundant or irrelevant data features which causes problems in network traffic classification. This retards the network traffic classification process and prevents to make accurate and optimal decisions when are dealing with big data. In this paper A hybrid feature selection algorithm is used for optimal feature classification and these mutual information based algorithms can handle both linearly and nonlinearly dependent data features. The results will be evaluated during network intrusion detection. The Least Square Support Vector Machine based IDS (LSSVM-IDS) which is an Intrusion Detection system and is developed using features of feature selection algorithm and its performance is evaluated using the data sets provided by KDD Cup 99 data sets. This improves accuracy and computational cost will be lowered as compared to other methods.
Cite this article:
Rekha Preethi M.C, Mr.Chetan R , " Least Square Support Vector Machine based IDS, using feature selection algorithm" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 6, Issue 3, May - June 2017 , pp. 064-068 , ISSN 2278-6856.
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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
ISSN 2278-6856
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