Snorkeling in Dark Waters: A Longitudinal Surface Exploration of Unique Tor Hidden Services

The Onion Router (Tor) is a controversial network whose utility is constantly under scrutiny. On the one hand, it allows for anonymous interaction and cooperation of users seeking untraceable navigation on the Internet. This freedom also attracts criminals who aim to thwart law enforcement investigations, e.g., trading illegal products or services such as drugs or weapons. Tor allows delivering content without revealing the actual hosting address, by means of .onion (or hidden) services. Different from regular domains, these services cannot be resolved by traditional name services, are not indexed by regular search engines, and they frequently change. This generates uncertainty about the extent and size of the Tor network and the type of content offered. In this work, we present a large-scale analysis of the Tor Network. We leverage our crawler, dubbed Mimir, which automatically collects and visits content linked, obtaining a dataset of 25k sites. We analyze the topology of the Tor Network, including its depth and reachability from the surface web. We define a set of heuristics to detect the presence of replicated content (mirrors) and show that most of the analyzed content in the Dark Web ( $\approx 82$ %) is a replica of another site. Also, we train a custom classifier to understand the type of content the hidden services offer. Overall, our study provides new insights into the Tor network, highlighting the importance of the initial seeding during the crawling process. We show that previous work on large-scale Tor measurements does not consider the presence of mirrors, which biases their understanding of the Dark Web topology and the distribution of content.