DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning
Arash Habibi Lashkari, Gurdip Kaur, Abir Rahali
Abstract
Darknet traffic classification is significantly important to categorize real-time applications. Although there are notable efforts to classify darknet traffic which rely heavily on existing datasets and machine learning classifiers, there are extremely few efforts to detect and characterize darknet traffic using deep learning. This work proposes a novel approach, named DeepImage, which uses feature selection to pick the most important features to create a gray image and feed it to a two-dimensional convolutional neural network to detect and characterize darknet traffic. Two encrypted traffic datasets are merged to create a darknet dataset to evaluate the proposed approach which successfully characterizes darknet traffic with 86% accuracy.