Traffic Classification over Tor Netwrok Based on RGB Images

The emergence of the Tor anonymous communication system can effectively protect users’ identities from being leaked by untrusted destinations and third parties on the Internet. However, there are endless cases of anonymous abuse using the Tor anonymous communication system to hide real identities and engage in cybercrime activities. Therefore, it is of great research significance to effectively identify Tor traffic.

To distinguish different categories of Tor traffic and different categories of regular traffic, traditional gray image data processing methods are widely used, but gray images cannot represent richer color information. In this regard, our paper proposes an RGB image data processing method and combines deep learning to classify Tor traffic. We first verify the impact of the image-saving format on model performance, then explore the impact of different assignment methods of the RGB image on our experimental results, and finally compare the performance of the model trained by the RGB image method and the conventional gray image method. Experimental results show that this method can effectively identify different Tor traffic and regular traffic with extremely high accuracy.