SVM based Traffic Classification for Mitigating HTTP Attack

Abstract

The advancement in Internet technology brings new dimension to commercial applications, entertainment
and information sharing. Consequently, many web services are launched in almost all needs of
the internet users. The development of effective network infrastructure increases the usage of these
services. 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 deeply
affects the availability of services. Categorizing the malicious traffic from normal traffic facilitates
the elimination process. In view of eliminating the most victimized attacks which deny the services
to 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 blocks
the computing resources to the legitimate users based on network flow properties. The network flow
properties are selected by the proposed optimization method. The simulated results exhibit that with
optimized feature set, the classification performance of the proposed classifier using RBF kernel is
competently higher when compared with other kernel models.