SVM based Traffic Classification for Mitigating HTTP Attack

Abstract

The advancement in Internet technology brings new dimension to commercial applications, entertainmentand information sharing. Consequently, many web services are launched in almost all needs ofthe internet users. The development of effective network infrastructure increases the usage of theseservices. However the convenience of using the web services are blocked by denial of service attack,which is the foremost web threat. This attack injects malicious traffics into the internet which deeplyaffects the availability of services. Categorizing the malicious traffic from normal traffic facilitatesthe elimination process. In view of eliminating the most victimized attacks which deny the servicesto the potential users, this paper proposes a classification method based on machine learning technique.The proposed SVM based classifier discriminates the HTTP attacks that intentionally blocksthe computing resources to the legitimate users based on network flow properties. The network flowproperties are selected by the proposed optimization method. The simulated results exhibit that withoptimized feature set, the classification performance of the proposed classifier using RBF kernel iscompetently higher when compared with other kernel models.