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Zhang, Y., Zeng, S., Huang, C.N., Fan, L., Yu, X., Dang, Y., Larson, C., Denning, D., Roberts, N. and Chen, H. (2010) Developing a Dark Web Collection and Infrastructure for Computational and Social Sciences. Proceedings of the IEEE International Conference on Intelligence and Security Informatics, Vancouver, 23-26 May 2010, 1-6.

has been cited by the following article:

• TITLE: Dark Web and Its Impact in Online Anonymity and Privacy: A Critical Analysis and Review

  AUTHORS: Arbër S. Beshiri, Arsim Susuri

  KEYWORDS: Dark Web, TOR, Privacy, Anonymity, I2P

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.3, March 20, 2019

  ABSTRACT: The Internet as the whole is a network of multiple computer networks and their massive infrastructure. The web is made up of accessible websites through search engines such as Google, Firefox, etc. and it is known as the Surface Web. The Internet is segmented further in the Deep Web—the content that it is not indexed and cannot access by traditional search engines. Dark Web considers a segment of the Deep Web. It accesses through TOR. Actors within Dark Web websites are anonymous and hidden. Anonymity, privacy and the possibility of non-detection are three factors that are provided by special browser such as TOR and I2P. In this paper, we are going to discuss and provide results about the influence of the Dark Web in different spheres of society. It is given the number of daily anonymous users of the Dark Web (using TOR) in Kosovo as well as in the whole world for a period of time. The influence of hidden services websites is shown and results are gathered from Ahimia and Onion City Dark Web’s search engines. The anonymity is not completely verified on the Dark Web. TOR dedicates to it and has intended to provide anonymous activities. Here are given results about reporting the number of users and in which place(s) they are. The calculation is based on IP addresses according to country codes from where comes the access to them and report numbers in aggregate form. In this way, indirect are represented the Dark Web users. The number of users in anonymous networks on the Dark Web is another key element that is resulted. In such networks, users are calculated through the client requests of directories (by TOR metrics) and the relay list is updated. Indirectly, the number of users is calculated for the anonymous networks.