A Review : Dark Web Using Machine Learning

Authors

  • Ankur Saxena  M Tech Scholar, Computer Science & Engineering, Millennium Institute of Technology and Science, Bhopal, India
  • Prof. Vinod Mahor  Assistant Professor, Computer Science & Engineering, Millennium Institute of Technology and Science Bhopal, India

Keywords:

Dark Web, Machine Learning, Illegal Activities, Detection, Supervised Learning

Abstract

The dark web is a part of the internet that is hidden from search engines and is accessible only through special software. It is a platform for illegal activities such as drug trafficking, cybercrime, and terrorist activities. Detecting and preventing such activities is a major challenge for law enforcement agencies. Machine learning is a rapidly growing field of computer science that has shown promise in detecting illegal activities on the dark web. In this paper, we review recent studies that have used machine learning techniques to detect illegal activities on the dark web. The review begins by discussing the challenges of detecting illegal activities on the dark web, including the anonymity of users and the use of encryption to hide communication. The review then discusses the different types of machine learning techniques that have been used to detect illegal activities, including supervised and unsupervised learning, deep learning, and natural language processing. Several studies have been conducted in recent years that have used machine learning techniques to detect illegal activities on the dark web. These studies have shown promising results, with machine learning algorithms achieving high accuracy rates in detecting illegal activities.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ankur Saxena, Prof. Vinod Mahor, " A Review : Dark Web Using Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.332-341, March-April-2023.