EBSNN: Extended Byte Segment Neural Network for Network Traffic Classification
Xi Xiao, Wentao Xiao, Rui Li, Xiapu Luo, Hai-Tao Zheng, Shu‐Tao Xia
Abstract
Network traffic classification is important to intrusion detection and network management. Most of existing methods are based on machine learning techniques and rely on the features extracted manually from flows or packets. However, with the rapid growth of network applications, it is difficult for these approaches to handle new complex applications. In this article, we design a novel neural network, the Extended Byte Segment Neural Network (EBSNN), to classify netwrk traffic. EBSNN first divides a packet into header segments and payload segments, which are then fed into encoders composed of the recurrent neural networks with the attention mechanism. Based on the outputs, another encoder learns the high-level representation of the whole packet. In particular, side-channel features are learned from header segments to improve the performance. Finally, the label of the packet is obtained by the softmax function. Furthermore, EBSNN can classify network flows by examining the first few packets. Thorough experiments on the real-world datasets show that EBSNN achieves better performance than the state-of-the-art methods in both the application identification task and the website identification task.