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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Enmob: Unveil the behavior with multi-flow analysis of encrypted app traffic
Cybersecurity, Vol.8(1), pp.1-17
20/04/2025
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Abstract
In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users' privacy. However, this encryption, while fortifying privacy, introduces challenges to security by hindering the effective management of network applications within encrypted data streams. Conventional detection methods for encrypted application traffic, relying heavily on statistical metrics like payload, packet size, and distribution, are constrained to single traffic flows, often yielding results of limited specificity. To address this limitation, our paper introduces an innovative approach that elucidates the multi-flow nature of application behavior traffic and provides context to encrypted application traffic. This method offers a more nuanced and comprehensive perspective for understanding and representing network traffic, even when encrypted. The efficacy of our approach was evaluated using a substantial volume of real network traffic data. Results indicate that our method achieves an average accuracy of 0.958 in identifying application behavior traffic and 0.955 in classifying application traffic. These outcomes signify a substantial enhancement over single network flow-based detection methods, demonstrating a notable 5.3% improvement.
Details
- Title
- Enmob: Unveil the behavior with multi-flow analysis of encrypted app traffic
- Creators
- Mengmeng Ge - Harbin Institute of TechnologyRuitao Feng - Singapore Management UniversityLikun Liu - Harbin Institute of TechnologyXiangzhan Yu - Harbin Institute of TechnologySachidananda Vinay - Supreme Council Of HealthXiaofei Xie - Singapore Management UniversityYang Liu - Supreme Council Of Health
- Publication Details
- Cybersecurity, Vol.8(1), pp.1-17
- Publisher
- Springer Nature
- Number of pages
- 17
- Grant note
- National Key Technologies Research and Development Program of China; National Key Technology R&D Program 2022YFC3321102 / Key Technologies Research and Development Program
- Identifiers
- 991013278305502368
- Copyright
- © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article