| Title | Dark web traffic detection method based on deep learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ma, Haoyu, Cao, Jianqiu, Mi, Bo, Huang, Darong, Liu, Yang, Zhang, Zhenyuan |
| Conference Name | 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) |
| Keywords | convolutional neural networks, dark web, data-driven, Deep Learning, Human Behavior, Learning systems, Network security, Network topology, Neural networks, privacy, pubcrawl, telecommunication traffic, Tor Traffic, Training |
| Abstract | Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%. |
| DOI | 10.1109/DDCLS52934.2021.9455619 |
| Citation Key | ma_dark_2021 |
Dark web traffic detection method based on deep learning