Hybrid Feature Selection Method using Fisher's Discriminate Ratio to Classify Internet Traffic Data

Identification and classification of network applications is a key area of network management and network security. This is due to exponential growth of the Internet users, which in turn increases the growth of the internet traffic. As Internet grows, different types of Internet traffic generated over the network also grow. This recommends proposing new methods to identify and classify the network traffic. In this paper, we proposed to embed Fishers's Discriminate Ratio (FDR) with Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and Plus L Minus R feature selection methods to analyze and classify the Internet traffic. To evaluate the proposed method, we used publicly available KDDcup99 dataset. Experimental results proved that the proposed embedding method will outperform compare to the existing methods.