Effect of Feature Selection on Performance of Internet Traffic Classification on NIMS Multi-Class dataset

The challenges faced by networks nowadays can be solved to a great extent by the application of accurate network traffic classification. Internet network traffic classification is responsible for associating network traffic with the application generating them and helps in the area of network monitoring, Quality of Service management, among other. Traditional methods of traffic classification including port-based, payload-load based, host-based, behavior-based exhibit a number of limitations that range from high computational cost to inability to access encrypted packets for the purpose of classification. Machine learning techniques based on statistical properties are now being employed to overcome the limitations of existing techniques. However, the high number of features of flows that serve as input to the learning machine poses a great challenge that requires the application of a pre-processing stage known as feature selection. Too many irrelevant and redundant features affect predictive accuracy and performance of the learning machine. This work analyses experimentally, the effect of a collection of ranking-basedfilter feature selection methods on a multi-class dataset for traffic classification. In the first stage, the proposed Top-N criterionis applied to the feature sets obtained, while in the second stage we generate for each Top-N set of features a new dataset which is applied as input to a set of four machine learning algorithms (classifiers).Experimental results show the viability of our model as a tool for selecting the optimal subset of features which when applied, lead to improvement of accuracy and performance of the traffic classification process.