Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine

Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2012 by IJETT Journal
Volume-3 Issue-4                          
Year of Publication : 2012
Authors :  Venkata Suneetha Takkellapati , G.V.S.N.R.V Prasad

Citation 

Venkata Suneetha Takkellapati , G.V.S.N.R.V Prasad. "Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine". International Journal of Engineering Trends and Technology (IJETT). V3(4):466-470 Jul-Aug 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

As the cost of the data processing and Internet accessibility increases, more and more organizations are be - coming vulnerable to a wide range of cyber threats. Most current offline intrusion detection systems are focused on unsupervised and supervised machine learning approaches. Existing model has high error rate during the attack classification usi ng support vector machine learning algorithm. Besides, with the study of existing work, feature selection techniques are also essential to improve high efficiency and effectiveness. Performance of different types of attacks detection should also be improv ed and evaluated using the proposed approach. In this proposed system, Information Gain (IG) and Triangle Area based KNN are used for selecting more discriminative features by combining Greedy k - means clustering algorithm and SVM classifier to detect Ne twork attacks. This system achieves high accuracy detection rate and less error rate of KDD CUP 1999 training data set.

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Keyword
Intrusion, IDS,data mining.