AI Summary: DeepTraffic is a toolkit that employs deep learning models specifically for the classification of network traffic, focusing on identifying malware and anomalies. Its primary application lies in enhancing cybersecurity measures through effective traffic analysis and representation learning using convolutional neural networks. Notable features include end-to-end encrypted traffic classification and the ability to learn hierarchical spatial-temporal features for improved intrusion detection.

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Deep Learning models for network traffic classification

For more information please read our papers.

:mortar_board:Wei Wang’s Google Scholar Homepage

Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng and Ming Zhu,“Malware Traffic Classification Using Convolutional Neural Networks for Representation Learning,” in the 31st International Conference on Information Networking (ICOIN 2017), pp. 712-717, 2017.

Wei Wang, Jinlin Wang, Xuewen Zeng, Zhongzhen Yang and Ming Zhu, “End-to-end Encrypted Traffic Classification with One-dimensional Convolution Neural Networks,” in the 15th IEEE International Conference on Intelligence and Security Informatics (IEEE ISI 2017), pp. 43-48, 2017.

Wei Wang, Yiqiang Sheng, Jinlin Wang, Xuewen Zeng, Xiaozhou Ye, Yongzhong Huang and Ming Zhu, “HAST-IDS: Learning Hierarchical Spatial-Temporal Features using Deep Neural Networks to Improve Intrusion Detection,” in IEEE Access, vol. 6, pp. 1792-1806, 2018.

Wei Wang, “Deep Learning for Network Traffic Classification and Anomaly Detection”, A Dissertation for Doctor’s Degree(Simplified Chineses), 2018.

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I’m a Ph.D. graduated from USTC, and I’m interested in network traffic analysis.
If you have an interesting job opportunity for me, please contact me at ww8137@mail.ustc.edu.cn or wechat/weixin:wangwei-science.