Darknet traffic classification and adversarial attacks using machine learning

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has em-ployed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities. This research aims to improve darknet traffic detection by as-sessing a wide variety of machine learning and deep learning techniques for the classification of such traf-fic and for classification of the underlying application types. We find that a Random Forest model outper-forms other state-of-the-art machine learning techniques used in prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our Random Forest classifier, we obfuscate select application type classes to simulate realistic adversarial attack scenarios. We demonstrate that our best-performing clas-sifier can be degraded by such attacks, and we consider ways to effectively deal with such adversarial attacks.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )