Abstract
With the popularity and development of mobile devices, the types and number of mobile applications are increasing, and people will perform different activities in the same application (e.g., refreshing the page, sending messages, browsing). Obtaining network traffic in real-time is an important foundation for network content supervision. Identifying application activity based on network traffic helps network managers understand user behaviors better and improve the quality of service. The widespread use of encrypted traffic in Mobile Applications presents a challenge to accurately identify application activities. Due to the low recognition speed of the existing application action recognition work for encrypted traffic, it is difficult to meet the real-time requirements. Therefore, we propose a fast application activity recognition method based on encrypted traffic. We extract the trend characteristics of the traffic generated by the application activity and use machine learning to identify the activity. The experimental results show that our method can identify user activities effectively. In addition, we have improved the existing method in real-time, and experiments show that our method is two orders of magnitude faster than the existing method when the recognition rate is similar to that of the existing methods.
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Acknowledgments
This work was supported by in part by the National Natural Science Foundation of China (Grant Nos. 61772559, 61602167).
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Liu, X., Zhang, S., Li, H., Wang, W. (2021). Fast Application Activity Recognition with Encrypted Traffic. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_25
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DOI: https://doi.org/10.1007/978-3-030-86130-8_25
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