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The unevenness importance of criminal activities in the onion domains of theTor Darknet and the different levels of their appeal to the end-user make themtangled to measure their influence. To this end, this paper presents a novelcontent-based ranking framework to detect the most influential onion domains.Our approach comprises a modeling unit that represents an onion domain usingforty features extracted from five different resources: user-visible text, HTMLmarkup, Named Entities, network topology, and visual content. And also, aranking unit that, using the Learning-to-Rank (LtR) approach, automaticallylearns a ranking function by integrating the previously obtained features.Using a case-study based on drugs-related onion domains, we obtained thefollowing results. (1) Among the explored LtR schemes, the listwise approachoutperforms the benchmarked methods with an NDCG of 0.95 for the top-10 rankeddomains. (2) We proved quantitatively that our framework surpasses thelink-based ranking techniques. Also, (3) with the selected feature, we observedthat the textual content, composed by text, NER, and HTML features, is the mostbalanced approach, in terms of efficiency and score obtained. The proposedframework might support Law Enforcement Agencies in detecting the mostinfluential domains related to possible suspicious activities.

Content-Based Features to Rank Influential Hidden Services of the Tor Darknet