Detection and classification of darknet traffic using machine learning methods

Graphical/Tabular In this study, a machine learning-based model has been developed for the detection and classification of the darknet or dark web that cybercriminals and attackers use to hide their identity information and provide encrypted communication. The statistical information of packets was analyzed using machine learning approach without deciphering encrypted network traffic. Feature selection was made to increase the performance of the model. In addition to this process, data balancing was performed in order to increase the detection and classification rate of features with low numbers during the training phase. The created model is given in Figure A.