Open Access

ARTICLE

TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks

Baoquan Liu^1,3, Xi Chen^2,3, Qingjun Yuan^2,3, Degang Li^2,3, Chunxiang Gu^2,3,*

1 School of Cyberspace Security, Zhengzhou University, Zhengzhou, 450002, China
2 School of Cyberspace Security, Information Engineering University, Zhengzhou, 450001, China
3 Henan Key Laboratory of Network Cryptography Technology, Zhengzhou, 450001, China

* Corresponding Author: Chunxiang Gu. Email:

Computers, Materials & Continua 2025, 82(2), 3179-3201. https://doi.org/10.32604/cmc.2024.059688

Received 14 October 2024; Accepted 03 December 2024; Issue published 17 February 2025

Abstract

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

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