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A green computing method for encrypted IoT traffic recognition based on traffic fingerprint graphs

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Abstract

The Internet of Things (IoT) is a revolutionary technological trend that is reshaping the way people interact with the digital world, creating limitless possibilities for future life and work. With the exponential increase in the number of IoT-connected devices, the substantial traffic data generated by smart devices poses significant challenges to traffic recognition, especially with the emergence of encrypted traffic for privacy protection. Encrypted traffic recognition is a prerequisite for ensuring Quality of Service (QoS) and network security. However, existing methods suffer from inadequate accuracy in encrypted traffic recognition and fail to address the issue of green computing of model training in IoT scenarios. In this paper, we propose a fast encrypted traffic recognition method, TFPGM, which is based on traffic fingerprint graphs to address these challenges. The core idea of TFPGM is to utilize interactive modes to represent the fingerprints of similar traffic types. TFPGM consists of two main phases: constructing traffic fingerprint graphs and establishing recognition models. In the construction phase, we analyze the interactive modes of encrypted traffic and represent them using undirected graphs. In the model-building phase, a dedicated recognizer is established for each traffic fingerprint to enhance the exploration of interactive modes within the same traffic class. These recognizers are combined to create a complete traffic fingerprint recognition model. In both phases, considerations were given to reducing the size of the graph and lowering the model’s complexity with the aim of achieving green computing in real-world scenarios. The results indicate that TFPG can complete model training in just 3.83 seconds while ensuring a 97.72% accuracy in the classification of 12 categories of encrypted traffic. Compared to existing methods, our method’s resource consumption can be reduced by over 90%, contributing to the realization of green computing in large-scale IoT scenarios. We also validate the model’s generalizability on another well-known encrypted traffic dataset, achieving a 95.63% accuracy in 14-class classification.

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Acknowledgements

This research was supported by the Young Scientists Fund of the National Natural Science Foundation of China under Grant (No. 62302322), the National Natural Science Foundation of China (No. U19A2081), the Fundamental Research Funds for the Central Universities (No. 2022SCU12116) and the Science and Engineering Connotation Development Project of Sichuan University (No. 2020SCUNG129).

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Authors and Affiliations

  1. Cyber Science Research Institute, Sichuan University, Chengdu, China

    Yao Li, Xingshu Chen & Wenyi Tang

  2. Key Laboratory of Data Protection and Intelligent Management (Sichuan University), Ministry of Education, Chengdu, China

    Xingshu Chen & Wenyi Tang

  3. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

    Yao Li, Xingshu Chen, Wenyi Tang & Bingyu Chen

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  1. Yao Li
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Correspondence to Wenyi Tang.

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Li, Y., Chen, X., Tang, W. et al. A green computing method for encrypted IoT traffic recognition based on traffic fingerprint graphs. Peer-to-Peer Netw. Appl. 17, 1514–1526 (2024). https://doi.org/10.1007/s12083-024-01665-3

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