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Toward effective mobile encrypted traffic classification through deep learning
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DOI：10.1016/j.neucom.2020.05.036.png)
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Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users' activity. Lastly, our framework is leveraged to point to future research perspectives. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Android apps
Deep learning
Encrypted traffic
iOS apps
Machine learning
Mobile apps
Privacy
Traffic classification
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6.5
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2.5W
被引数:
6.5W
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Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection FrameworkDeep-Full-Range: 基于深度学习的网络加密流量分类和入侵检测框架.png)
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