Optimizing Traffic Classification Using Hybrid Feature Selection

The identification of network applications is of fundamental important to numerous network activities. Unfortunately, traditional port-based classification and packet payload-based analysis exhibit a number of shortfalls. A promising alternative is to use Machine Learning (ML) techniques and identify network applications based on per-flow features. Since a lot of flow features can be used for flow classification, the flow classifier may deal with huge amount of data, which contains irrelevant and redundant features causing slower training and testing process, higher resource consumption as well as poor classification accuracy. Therefore, feature selection plays a vital role in performance optimizing. In this paper, we propose a hybrid feature selection method for flow classification using Chi-Squared and C4.5 algorithm (ChiSquared-C4.5). The experiments demonstrate our approach can greatly improve computational performance without negative impact on classification accuracy.