An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web

Abstract

Traffic classification plays an important role in network management and cyber-security. With the development of the Internet, online applications and in the following encrypted techniques, encrypted traffic has changed to a major challenge for traffic classification. In fact, unbalanced data, in which the unbalanced distribution of samples across classes lead to the classification performance reduction, is considered as one of the prominent challenges in encrypted traffic classification. Although previous studies tried to deal with the class imbalance problem in the pre-processing step using machine learning and particularly deep learning models, they are still confronting with some limitations. In this regard, a new classification method is proposed in this paper that tries to deal with the problem of unbalanced data during the training process. The proposed method employs a cost-sensitive convolution neural network and considers a cost for each classification according to the distribution of classes. These costs are then applied to the network along the training process to enhance the overall accuracy. Based on the empirical results, the proposed model obtained higher classification performance (about 2% on average) compared to the Deep Packet method.