Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Yu Zhang; Zhenhong Jia; Xiaohui Huang; Jiajia Wang; Gang Zhou; Fei Shi; Changwu Lu

doi:10.1117/12.3035287

19 July 2024 Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Yu Zhang, Zhenhong Jia, Xiaohui Huang, Jiajia Wang, Gang Zhou, Fei Shi, Changwu Lu

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131S (2024) https://doi.org/10.1117/12.3035287
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Internet traffic analysis is the core approach to network management and security. In the rapidly changing environment of encrypted traffic, traditional plaintext-based analysis methods have become obsolete. Although there are currently some methods for analysing encrypted traffic, they overlook the inherent logic and hierarchy of different encrypted traffic analysis requirements and lack research into the essential characteristics of encrypted traffic. This article proposes a framework FM-ENet based on Graph Neural Network and Deep learning for Tor encrypted traffic classification to meet the practical needs of network management and security monitoring. A Flow Information Graph has been designed on the basis of Graph Neural Network, which can effectively alleviate the problem of concept drift. In the area of deep neural networks, we have developed an end-to-end multi-level spatio-temporal feature fusion enhanced network module ST-FENet, which has the advantage of automatically learning the non-linear relationship between input and output data without the need for manual feature extraction, this structure can complement each other in classification performance and solve the problem of low classifier efficiency. We compared FM-ENet to current popular methods using the public ISCX-Tor dataset. FM-ENet can achieve higher performance while saving costs, with a 9% improvement in accuracy compared to FlowPrint and a 4.4%, 4.5% and 4.4% improvement in PR, RC and F1 indicators compared to TSCRNN.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Zhang, Zhenhong Jia, Xiaohui Huang, Jiajia Wang, Gang Zhou, Fei Shi, and Changwu Lu "Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131S (19 July 2024); https://doi.org/10.1117/12.3035287

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KEYWORDS

Feature extraction

Deep learning

Analytical research

Machine learning

Network security

Neural networks

Information security

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