Review on Dark Web and Its Impact on Internet Governance
Keywords:Keywords: Dark Web, Internet Governance
Cyber attackers use the Dark Web, a collection of facilities that are not visible to search engines and normal users, to explore a variety of illegal products and services. In this paper, the Dark Web and its impact on internet governance were analyzed. The findings of a review of the literature provide in-depth knowledge on the increasing number of crimes committed on the Dark Web, considering the economic, social, along ethical consequences of cybercrime on the Dark Web, as well as analyzing the consequences and methods for locating the criminals, as well as their drawbacks. Fraudsters, militants, and government-sponsored secret agents used the Dark Web where is among the most popular difficult together with unidentifiable channels to achieve their illicit goals. Crimes that were committed on the Dark Web are similar to criminal offenses committed in the real world. Nevertheless, the sheer size of the Dark Web, the unpredictability of the ecosystem, as well as the privacy and confidentiality afforded by Dark Web services, were also critical challenges in tracing criminals. Measuring the yachting Dark Web crime risks is a critical step in discovering alternative approaches to cybercrime. The study reveals that Dark Web services are available to arrest criminals, as well as digital facts and evidence, should be analyzed and applied in a way that allows Internet Governance.
Alharbi, A., Faizan, M., Alosaimi, W., Alyami, H., Agrawal, A., Kumar, R., & Ahmad Khan, R. (2021). Exploring the topological properties of the Tor Dark Web. IEEE Access, 9, 21746-21758. https://doi.org/10.1109/ACCESS.2021.3055532
Alkhatib, B, & Basheer, R. (2019a). Mining the Dark Web: A novel approach for placing a Dark Website under investigation. International Journal of Modern Education and Computer Science, 11(10), 1-13. https://doi.org/10.5815/ijmecs.2019.10.01
Alkhatib, B., & Basheer, R. (2019b). Crawling the Dark Web: A conceptual perspective, challenges and implementation. Journal of Digital Information Management, 17(2), 51-60. https://doi.org/10.6025/jdim/2019/17/2/51-60
Beshiri, A., & Susuri, A. (2019). Dark Web and its impact in online anonymity and privacy: A critical analysis and review. Journal of Computer and Communications. 7. 30-43. https://doi.org/10.4236/jcc.2019.73004
Chertoff, M. (2017). A public policy perspective of the Dark Web. Journal of Cyber Policy. 2. 1-13. https://doi.org/10.1080/23738871.2017.1298643
Davies, G. (2020). Shining a light on policing of the Dark Web: An analysis of UK investigatory powers. The Journal of Criminal Law, 84(5), 407–426. https://doi.org/10.1177/0022018320952557
East, C. S. (2017). Demystifying the Dark Web. ITNOW, 59(1), 16-17. https://doi.org/10.1093/itnow/bwx007
Easttom, C. (2018). Conducting investigations on the Dark Web. Journal of Information Warfare, 17(4), 26-37. https://doi.org/doi:10.2307/26783825
Ferry, N., Hackenheimer, T., Herrmann, F., & Tourette, A. (2019, June 27-29). Methodology of dark web monitoring, ECAI 2019: Pitesti, Romania. https://doi.org/10.1109/ECAI46879.2019.9042072
Godawatte, K., Raza, M., Murtaza, M., & Saeed, A. (2019, Dec 5-7). Dark Web along with The Dark Web marketing and surveillance [Paper presentation]. PDCAT 2019: Gold Coast, Australia.
Kadoguchi, M., Hayashi, S., Hashimoto, M., & Otsuka, A. (2019, 1-3 July). Exploring the Dark Web for cyber threat intelligence using machine leaning. ISI 2019: Shenzhen, China. https://doi.org/10.1109/ISI.2019.8823360
Montieri, A., Ciuonzo, D., Bovenzi, G., Persico, V., & Pescapé, A. (2020). A dive into the Dark Web: Hierarchical traffic classification of anonymity tools. IEEE Transactions on Network Science and Engineering, 7(3), 1043-1054. https://doi.org/10.1109/TNSE.2019.2901994
Nazah, S., Huda, S., Abawajy, J, & Hassan, M. M. (2020). Evolution of Dark Web threat analysis and detection: A systematic approach. IEEE Access, 8, 171796-171819. https://doi.org/10.1109/ACCESS.2020.3024198
Omar, Z. M., & Ibrahim, J. (2020). An overview of Darknet, rise and challenges and its assumptions. International Journal of Computer Science and Information Technology, 8(3), 110-116.
Rafiuddin, M. F., Minhas, H., & Dhubb, P. S. (2017, Sept 21-22). A Dark Web story in-depth research and study conducted on the dark web based on forensic computing and security in Malaysia. IEEE ICPCSI 2017: Chennai, India. https://doi.org/10.1109/ICPCSI.2017.8392286.
Robertson, J., Diab, A., Marin, E., Nunes, E., Paliath, V., Shakarian, J., & Shakarian, P. (2017). Darkweb cyber threat intelligence mining. Journal of Computer Science and Information Technology, 15(2), 28-43. https://doi.org/10.1017/9781316888513
Schäfer, M., Fuchs, M., Strohmeier, M., Engel, M., Liechti, M., & Lenders, V. (2019, 28-31 May). BlackWidow: Monitoring the Dark Web for cyber security information. CyCon 2019: Tallinn, Estonia. https://doi.org/10.23919/CYCON.2019.8756845
Topor, L. (2019a). Dark Hatred: Antisemitism on the Dark Web. Journal of Contemporary Antisemitism, 2, 25-42. https://doi.org/10.26613/jca/2.2.31.
Topor, Lev. (2019b). Dark and Deep Webs-Liberty or Abuse. International Journal of Cyber Warfare and Terrorism, 9, 1-14. https://doi.org/10.4018/IJCWT.2019040101.
Yang, Y., Yang, L., Yang, M., Yu, H., Zhu, G., Chen, Z., & Chen, L. (2019, May 24-26). Dark Web forum correlation analysis research. ITAIC 2019: Chongqing, China. https://doi.org/10.1109/ITAIC.2019.8785760
Zhang, N., Ebrahimi, M., Li, W., & Chen, H. (2020, Nov 9-10). A generative adversarial learning framework for breaking text-based CAPTCHA in the Dark Web. ISI 2020: Arlington, VA, USA. https://doi.org/10.1109/ISI49825.2020.9280537
Zhang, X., & Chow, K. (2018). A framework for Dark Web threat intelligence analysis. International Journal of Digital Crime and Forensics (IJDCF), 10(4), 108-117. http://doi.org/10.4018/IJDCF.2018100108