The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. We consider that our new algorithm, the extended dataset, and the findings obtained from the analysis carried out are helpful for LEAs to fight against crimes that take place in the Tor hidden services. We also discovered that domains related to suspicious activities usually present multiple clones under different addresses, what could be used as an additional feature for identifying them. The analysis of DUTA-10K reveals that only 20% of the hidden services that can be accessed are related to suspicious activities, and 48% are associated with normal ones. Results showed that our proposal obtains a higher harm to the Tor network robustness than all of them, what indicates its superiority for this problem. We quantitatively compared ToRank with some of the most popular ranking algorithms, like PageRank, HITS, and Katz. We also thoroughly analyze the content present in Tor, creating a dataset, DUTA-10K, that extends the previous Darknet Usage Text Address (DUTA) dataset. In this paper, we propose a new algorithm, named ToRank, that ranks hidden services in Tor better than the known algorithms used for the Surface Web. Law Enforcement Agencies need to monitor and to investigate crimes hidden behind the anonymity provided by Tor. The Tor network hosts a significant amount of hidden services related to suspicious activities.
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