Crime, terrorism, and other illegal activities are increasingly taking place in cyberspace. Crime in the dark web is one of the most serious challenges confronting governments around the world. Dark web makes it difficult to detect criminals and track activities, as it provides anonymity due to special tools such as TOR. Therefore, it has evolved into a platform that includes many illegal activities such as pornography, weapon trafficking, drug trafficking, fake documents, and more specially terrorism as in the context of this paper. Dark web studies are critical for designing successful counter-terrorism strategies. The aim of this research is to conduct a critical analysis of the literature and to demonstrate research efforts in dark web studies related to terrorism. According to result of study, the scientific studies related to terrorism activities have been minimally conducted and the scientific methods used in detecting and combating them in dark web should be varied. Advanced artificial intelligence, image processing and classification by using machine learning, natural language processing methods, hash value analysis, and sock puppet techniques can be used to detect and predict terrorist incidents on the dark web.
Primary Language | English |
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Subjects | Software Testing, Verification and Validation, Software Engineering (Other) |
Journal Section | Articles |
Authors | |
Publication Date | April 30, 2022 |
Submission Date | June 10, 2021 |
Acceptance Date | November 8, 2021 |
Published in Issue | Year 2022 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License