Discerning the traffic in anonymous communication networks using machine learning: concepts, techniques and future trends

With the growing need for anonymity and privacy on the internet, anonymous communication networks (ACNs) such as Tor, I2P, JonDonym, and Freenet have risen to fame. Such anonymous networks aim to provide freedom of expression and protection against tracking to its users. Simultaneously, there is also a class of users involved in the illegal usage of these ACNs. An emerging research topic in the field of ACNs is network traffic classification, as it can improve the network security against illegal users as well as improve the quality of service for its legal users. In this study, we review the research works available in the literature relevant to traffic classification in ACNs based on machine learning (ML) and also present to the researchers the general concepts and techniques in this area. A discussion on future trends in this area is also provided to bring out the future enhancements required in ML-based network traffic classification in ACNs.