Classification Method of Encrypted Traffic Based on Deep Neural Network

Author:

Wan Jing,Wu Libing,Xia Youhua,Hu Jianzong,Xia Zhenchang,Zhang Rui,Wang Min

Publisher

Springer Singapore

Reference24 articles.

1. Korczynski, M., Duda, A.: Markov chain finger printing to classify encrypted traffic. In: 2014 Proceedings of IEEE INFOCOM, pp. 781–789 (2014)

2. Shen, M., Wei, M., Zhu, L., Wang, M.: Classification of encrypted traffic with second-order Markov chains and application attribute bigrams. IEEE Trans. Inf. Forensics Secur. 12(8), 1830–1843 (2017)

3. Zhang, Z., Li, J., Manikopoulos, C.N., Jorgenson, J., Ucles, J.: HIDE: a hierarchical network intrusion detection system using statistical pre-processing and neural network classification. In: Proceedings of IEEE Workshop on Information Assurance and Security, pp. 85–90 (2001)

4. Wang, W., Zhu, M., Wang, J., Zeng, X., Yang, Z.: End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In: 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 43–48 (2017)

5. Lotfollahi, M., Zade, R.S.H., Siavoshani, M.J., Saberian, M.: Deep packet: a novel approach for encrypted traffic classification using deep learning. arXiv preprint arXiv:1709.02656 (2017)

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