An encrypted traffic classification model based on the raw traffic and spatiotemporal characteristics
Guanglong Zhao, Zhen Wang, Ziheng Yang · 2022
Deep learning techniques are frequently utilized and produce effective results in the classification of encrypted traffic. In the current encryption traffic classification process, the network traffic characteristics are not sufficiently extracted, which is a concern. An encrypted traffic classification model based on raw network traffic and its spatiotemporal characteristics is proposed in this paper. The raw network traffic is divided into sessions, and the packets inside each session are then split into 784-byte slices, and the traffic is then described using the slice data. The time feature vector and the spatial feature vector are then created by combining ResNet and GRU models to generate features from raw network data in parallel. The traffic is then classified using the combined features. According to experimental findings, the proposed model's recognition accuracy on the ISCX-NonVPN-VPN2016 dataset reached 99.36%, which is an improvement over other approaches currently in use.