Evaluation of feature selection on network traffic classification

Abstract

Malicious traffic classification has become a challenge in modern communications. It is a very important task for a trained model to successfully distinguish malicious traffic. With the gradual application of machine learning and deep learning in the field of traffic classification, traffic classification has reached a high accuracy rate. Feature selection can lighten models and improve classification performance by selecting the optimal sub-feature set. Therefore, the selection of effective features is an important issue for malicious traffic classification. In this article, we propose the idea of applying feature selection methods Information Gain and RFE to malicious traffic classification. The essence is to select an effective and optimal sub-feature set from a large number of features to characterize network traffic. Then, we used the deep learning method CNN and the machine learning method RF on the three real network traffic datasets of CICIDS2017, NSL-KDD and UNSW-NB15 respectively to evaluate and verify. The experiment shows that the combination of CNN and Information Gain has the best effect. The results of many experiments show that the performance of traffic classification is greatly improved after feature selection.