A survey of methods for encrypted traffic classification and analysis

By P. Velan, Milan Cermák, Pavel Čeleda, Martin Drašar. International Journal of Network Management, 2015

350 citations recorded by the source corpus.

With the widespread use of encrypted data transport, network traffic encryption is becoming a standard nowadays. This presents a challenge for traffic measurement, especially for analysis and anomaly detection methods, which are dependent on the type of network traffic. In this paper, we survey existing approaches for classification and analysis of encrypted traffic. First, we describe the most widespread encryption protocols used throughout the Internet. We show that the initiation of an encrypted connection and the protocol structure give away much information for encrypted traffic classification and analysis. Then, we survey payload and feature‐based classification methods for encrypted traffic and categorize them using an established taxonomy. The advantage of some of described classification methods is the ability to recognize the encrypted application protocol in addition to the encryption protocol. Finally, we make a comprehensive comparison of the surveyed feature‐based classification methods and present their weaknesses and strengths. Copyright © 2015 John Wiley & Sons, Ltd.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5ccbc2cf6f324d8b5950bf0484878f853a50dc51

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Citations in the corpus

FS-Net: A Flow Sequence Network For Encrypted Traffic Classification (2019, background): “However, encrypted traffic classification is a huge challenge to traditional rule-based methods, due to the fact that all the communication contents are randomized after encryption [4].”
Peek-a-boo: i see your smart home activities, even encrypted! (2018, methodology): “, packet lengths, traffic rate) of the network traffic still leaks some information about the messages exchanged [11, 13, 25, 44, 45, 48].”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “In this context, [7] reports a comprehensive review of several studies focused on encrypted traffic.”
Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, background): “First, the increasing demand for user’s privacy and data encryption has tremendously raised the amount of encrypted traffic in today’s Internet (Velan et al. 2015).”
End-to-end encrypted traffic classification with one-dimensional convolution neural networks (2017, methodology): “According to the difference of ISO/OSI layer, traffic encryption techniques can be divided into application layer encryption, presentation layer encryption and network layer encryption [2].”