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.

翻译：匿名工具的主要目标是通过实现强大的加密和混淆技术来保护用户的匿名性。因此，监控和识别这些网络上的用户活动变得非常困难。此外，此类系统具备强大的防御机制，以保护用户免受潜在风险，包括流量特征提取和网站指纹识别。然而，强大的匿名特性也为那些试图在网络中避免被追踪的非法活动参与者提供了庇护。因此，大量研究已采用机器学习技术来检查和分类加密流量。本文全面审视了当前用于分类暗网内部匿名流量及加密网络流量的现有方法。同时，本文还全面分析了利用机器学习技术监控和识别暗网内部流量攻击的方法。