A novel method for mobile application recognition in encrypted channels

Jiangtao Zhai; Zihao Wang; Kun Duan; Tao Wang; Jiangtao Zhai; Zihao Wang; Kun Duan; Tao Wang

doi:10.3934/era.2024010

Electronic Research Archive

2024, Volume 32, Issue 1: 193-223. doi: 10.3934/era.2024010

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A novel method for mobile application recognition in encrypted channels

1.
School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Key Laboratory of Intelligent Support Technology for Complex Environments, Ministry of Education, Nanjing 210044, China

Received: 15 September 2023 Revised: 11 December 2023 Accepted: 13 December 2023 Published: 20 December 2023

In the field of mobile application traffic analysis, existing methods for accurately identifying encrypted traffic often encounter challenges due to the widespread adoption of encryption channels and the presence of background traffic. Consequently, this study presents a novel mobile application traffic identification model that is in encrypted channels. The proposed model utilizes an adaptive feature extraction technique that combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to effectively extract abstract features from encrypted mobile application traffic. Additionally, by employing a probability-based comprehensive analysis to filter out low-confidence background traffic interference, the reliability of recognition is further enhanced. Experimental comparisons are conducted to validate the efficacy of the proposed approach. The results demonstrate that the proposed method achieves a remarkable classification accuracy of 95.4% when confronted with background traffic interference, surpassing existing techniques by over 15% in terms of anti-interference performance.
- mobile application traffic analysis,
- encryption channels,
- Convolutional Neural Networks,
- Gated Recurrent Neural Networks,
- background traffic interference
Citation: Jiangtao Zhai, Zihao Wang, Kun Duan, Tao Wang. A novel method for mobile application recognition in encrypted channels[J]. Electronic Research Archive, 2024, 32(1): 193-223. doi: 10.3934/era.2024010

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Abstract

In the field of mobile application traffic analysis, existing methods for accurately identifying encrypted traffic often encounter challenges due to the widespread adoption of encryption channels and the presence of background traffic. Consequently, this study presents a novel mobile application traffic identification model that is in encrypted channels. The proposed model utilizes an adaptive feature extraction technique that combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to effectively extract abstract features from encrypted mobile application traffic. Additionally, by employing a probability-based comprehensive analysis to filter out low-confidence background traffic interference, the reliability of recognition is further enhanced. Experimental comparisons are conducted to validate the efficacy of the proposed approach. The results demonstrate that the proposed method achieves a remarkable classification accuracy of 95.4% when confronted with background traffic interference, surpassing existing techniques by over 15% in terms of anti-interference performance.

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