  The primary objective of an anonymity tool is to protect the anonymity of its
users through the implementation of strong encryption and obfuscation
techniques. As a result, it becomes very difficult to monitor and identify
users activities on these networks. Moreover, such systems have strong
defensive mechanisms to protect users against potential risks, including the
extraction of traffic characteristics and website fingerprinting. However, the
strong anonymity feature also functions as a refuge for those involved in
illicit activities who aim to avoid being traced on the network. As a result, a
substantial body of research has been undertaken to examine and classify
encrypted traffic using machine learning techniques. This paper presents a
comprehensive examination of the existing approaches utilized for the
categorization of anonymous traffic as well as encrypted network traffic inside
the darknet. Also, this paper presents a comprehensive analysis of methods of
darknet traffic using machine learning techniques to monitor and identify the
traffic attacks inside the darknet.
