Encrypted Network Traffic Identification Based on 2D-CNN Model
Yan Zhou, Huiling Shi, Yuhan Zhao, Wei Gao, Wei Zhang
Abstract
Rapid development of the Internet has enabled explosive growth of various network traffic. How to classify and identify different categories of network traffic among these huge network traffic for cyberspace security has always been a hot research topic. In our study, we found that the composition structure of data frames and grayscale maps in the original traffic is very similar. Combined with recent research of deep learning in image processing, this paper proposes a 2D-CNN model-based network traffic recognition algorithm, while transforming traffic to grayscale maps for recognition. To validate the effectiveness of our proposed model, we use the public network dataset ISCX-VPN-NonVPN-2016 and USTC-TF2016. Experimental results prove that the average accuracy is 98.7% in regular encrypted traffic identification and 97.6% for malicious traffic identification. Our method provides new solutions for network traffic identification.