Encrypted traffic identification

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, 2020
In recent years, due to the need of privacy protection, the proportion of encrypted traffic in the network continues to growth. Traditional network traffic identification methods for non-encrypted traffic are difficult to use to encrypted traffic, therefore, analysis methods suitable for encrypted traffic have become a new research hotspot in network ...
Rui Liu, Xiangzhan Yu
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Deep learning-based real-time VPN encrypted traffic identification methods

Journal of Real-Time Image Processing, 2019
With the widespread application of virtual private network (VPN) technology, real-time VPN traffic identification has become an increasingly important task in network management and security maintenance. Since traditional encrypted traffic identification technology is not effective in feature extraction and selection, this paper proposes two deep ...
Lulu Guo +5 more
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FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019
It is crucial to accurately identify the type of traffic and application so that it can enable various policy-driven network management and security monitoring. However, with the increasing adoption of Internet applications use encryption protocols to transmit data, traffic classification is becoming more difficult.
ZiXuan Wang +4 more
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Learning from imbalanced data for encrypted traffic identification problem

Proceedings of the Seventh Symposium on Information and Communication Technology, 2016
Identifying encrypted application traffic is an important issue for many network tasks including quality of service, firewall enforcement and security. One of the challenging problems of classifying encrypted application traffic is the imbalanced property of network data.
Ly Vu, Dong Van Tra, Quang Uy Nguyen
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encrypted traffic
deep learning
machine learning

encrypted traffic classification
convolutional neural network
traffic identification

multi-feature fusion
bayesian optimization
network traffic classification