Research Article
MAAN: A Multiple Attribute Association Network for Mobile Encrypted Traffic Classification
@INPROCEEDINGS{10.1007/978-3-030-63086-7_11, author={Fengzhao Shi and Chao Zheng and Yiming Cui and Qingyun Liu}, title={MAAN: A Multiple Attribute Association Network for Mobile Encrypted Traffic Classification}, proceedings={Security and Privacy in Communication Networks. 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2020}, month={12}, keywords={Encrypted traffic classification SSL/TLS Handshake messages Application Data Network management}, doi={10.1007/978-3-030-63086-7_11} }- Fengzhao Shi
Chao Zheng
Yiming Cui
Qingyun Liu
Year: 2020
MAAN: A Multiple Attribute Association Network for Mobile Encrypted Traffic Classification
SECURECOMM
Springer
DOI: 10.1007/978-3-030-63086-7_11
Abstract
With the rapid development of mobile applications and the rising concern over user privacy, cryptographic protocols, especially Secure Socket Layer/Transport Layer Security (SSL/TLS), are widely used on the Internet. Many networking and security services call for application-level encrypted traffic classification before conducting related policies. Exiting methods exhibit unsatisfying accuracy using the partial handshake information or only the flow-level features. In this paper, we propose a novel encrypted traffic classification method named Multiple Attribute Associate Network (MAAN). MAAN is a unified model that automatically extracts features from handshake messages and flows. Moreover, the MAAN has acceptable time consumption and is suitable to apply in real-time scenarios. Our experiments demonstrate that the MAAN achieves(98.2\%)accuracy on a real-word dataset (including 59k+ SSL sessions and covering 16 applications) and outperforms the state-of-the-art methods.
