An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification

Substantial recent efforts have been made on the application of Machine Learning (ML) techniques to flow statistical features for traffic classification. However, the classification performance of ML techniques is severely degraded due to the high dimensionality and redundancy of flow statistical features, the imbalance in the number of traffic flows and concept drift of Internet traffic. With the aim of comprehensively solving these problems, this paper proposes a new feature optimization approach based on deep learning and Feature Selection (FS) techniques to provide the optimal and robust features for traffic classification. Firstly, symmetric uncertainty is exploited to remove the irrelevant features in network traffic data sets, then a feature generation model based on deep learning is applied to these relevant features for dimensionality reduction and feature generation, finally Weighted Symmetric Uncertainty (WSU) is exploited to select the optimal features by removing the redundant ones. Based on real traffic traces, experimental results show that the proposed approach can not only efficiently reduce the dimension of feature space, but also overcome the negative impacts of multi-class imbalance and concept drift problems on ML techniques. Furthermore, compared with the approaches used in the previous works, the proposed approach achieves the best classification performance and relatively higher runtime performance.