Darknet Traffic Analysis: A Systematic Literature Review
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 s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10460558/ |
| _version_ | 1827304688982163456 |
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| author | Javeriah Saleem Rafiqul Islam Md. Zahidul Islam |
| author_facet | Javeriah Saleem Rafiqul Islam Md. Zahidul Islam |
| author_sort | Javeriah Saleem |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T17:42:11Z |
| format | Article |
| id | doaj.art-d2be27c265e3424b89c63f614f11f9d6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T17:42:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d2be27c265e3424b89c63f614f11f9d62024-03-27T23:00:24ZengIEEEIEEE Access2169-35362024-01-0112424234245210.1109/ACCESS.2024.337376910460558Darknet Traffic Analysis: A Systematic Literature ReviewJaveriah Saleem0https://orcid.org/0000-0001-6510-3279Rafiqul Islam1https://orcid.org/0000-0001-8317-5727Md. Zahidul Islam2https://orcid.org/0000-0002-4868-4945School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Albury, NSW, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, AustraliaThe 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.https://ieeexplore.ieee.org/document/10460558/Cyberattackcyber threat intelligencedark webdata privacydata securitynetwork security |
| spellingShingle | Javeriah Saleem Rafiqul Islam Md. Zahidul Islam Darknet Traffic Analysis: A Systematic Literature Review IEEE Access Cyberattack cyber threat intelligence dark web data privacy data security network security |
| title | Darknet Traffic Analysis: A Systematic Literature Review |
| title_full | Darknet Traffic Analysis: A Systematic Literature Review |
| title_fullStr | Darknet Traffic Analysis: A Systematic Literature Review |
| title_full_unstemmed | Darknet Traffic Analysis: A Systematic Literature Review |
| title_short | Darknet Traffic Analysis: A Systematic Literature Review |
| title_sort | darknet traffic analysis a systematic literature review |
| topic | Cyberattack cyber threat intelligence dark web data privacy data security network security |
| url | https://ieeexplore.ieee.org/document/10460558/ |
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