학술논문
Darknet Traffic Analysis: A Systematic Literature Review
Document Type
Periodical
Author
Source
IEEE Access Access, IEEE. 12:42423-42452 2024
Subject
Language
ISSN
2169-3536
Abstract
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 ML (machine learning) techniques to monitor and identify the traffic attacks inside the darknet.