Darknet Detection: A Darknet Traffic Detection Method Based on Improved Between-class Learning

Abstract

With the development of the Internet, people have paid more attention to privacy protection, and privacy protection technology is widely used. However, it also breeds the darknet, which has become a tool that criminals can exploit, especially in the fields of economic crime and military intelligence. The darknet detection is becoming increasingly important, however, the darknet traffic is seriously unbalanced. The detection is difficult and the accuracy of the detection methods needs to be improved. To overcome these problems, in this paper, we first propose a novel learning method. The method is a Chebyshev distance based Between-class learning (CDBC), which can learn the spatial distribution of the darknet dataset, and generate "gap data". The gap data can be used to optimize the distribution boundaries of the dataset. Secondly, a novel darknet traffic detection scheme is proposed. We test the proposed method on the ISCXTor 2016 dataset and the CIC-Darknet 2020 dataset, and the results show that CDBC can help more than 10 existing methods improve accuracy, even up to 99.99%. Compared with other sampling methods, CDBC can also help the classifiers achieve higher recall.