Shadowsocks Traffic Identification Based on Convolutional Neural Network
- Resource Type
- Conference
- Authors
- Zliang, Nan; Wu, Tiantian; Zhang, Yuening; Xiao, Mingzhong
- Source
- 2020 International Conference on Information Science and Education (ICISE-IE) ICISE-IE Information Science and Education (ICISE-IE), 2020 International Conference on. :480-485 Dec, 2020
- Subject
- Computing and Processing
Information science
Neural networks
Telecommunication traffic
Data models
Encryption
Virtual private networks
Internet
component
traffic recognition
shadowsocks
convolutional neural network
- Language
Network traffic has been massively produced since the development of the Internet. While the encryption of traffic has ensured the security and reliability of information, it also brings great challenge to the traffic identification and monitoring. The present study proposes a method of shadowsocks traffic identification based on the one-dimensional Convolutional Neural Network. This method simplifies the feature extraction of traffic identification and the recognition accuracy is over 98%. Because we can not find the published shadowsocks traffic dataset, we gathered four encryption kinds of shadowsocks traffic to study on the influence of different encryption on shadowsocks traffic. Moreover, we include VPN traffic and do contrast experiment based on four deep-learning models to verify the efficiency of one-dimensional convolutional neural network.