arxiv.org
Study of whether website fingerprinting can distinguish and identify Tor onion-service traffic.
Mimir Onion Study
An arXiv paper presenting a large-scale analysis of the Tor network using a crawler called Mimir, which collects and visits linked content. The authors report a dataset of pages from more than 25k sites and examine network topology, replicated content and content types.
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Study of whether website fingerprinting can distinguish and identify Tor onion-service traffic.
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0This arXiv paper proposes an end-to-end scalable architecture for early identification of new Tor sites and daily analysis of their content. It is built on an open source big data stack including Kubernetes, Kafka, Kubeflow and MinIO, discovering onion addresses across several sources, downloading HTML from Tor, deduplicating with MinHash LSH and categorizing with BERTopic modeling. In 93 days it identified 80,049 onion services and characterized 90% of them.
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0Public-health research measuring harmful-content availability and searches on the Tor network.
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0CRATOR is a proposed dark web crawler described in an arXiv paper. It is designed to extract pages that handle security protocols such as captchas, using seed URL lists, link analysis and scanning to discover content, with user-agent rotation and proxy usage for anonymity. The authors report evaluation using coverage, performance and robustness metrics, and suggest threat intelligence, cybersecurity and online investigation applications.
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0An arXiv paper measuring onion website discovery and Tor users' interests with honeypots. The authors deployed honeypot onion websites in March–April 2025 and seeded links via three channels to observe interaction events. They report that human users almost without exception originated from Ahmia.fi, and that the CSAM-themed honeypot drew markedly higher engagement than the others.
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0This dataset maps the network layer of the Invisible Internet Project (I2P), collected using the SWARM-I2P framework, which deployed I2P routers as mapping agents. It documents over 50,000 nodes with metrics such as bandwidth, latency, and uptime, plus network traffic records and geographic distribution. Node identifiers were anonymized. Data is available in CSV and TXT formats on Zenodo, with mapping scripts on GitHub.
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More in Dark Web Research & Studies
arxiv.org
This arXiv paper proposes an end-to-end scalable architecture for early identification of new Tor sites and daily analysis of their content. It is built on an open source big data stack including Kubernetes, Kafka, Kubeflow and MinIO, discovering onion addresses across several sources, downloading HTML from Tor, deduplicating with MinHash LSH and categorizing with BERTopic modeling. In 93 days it identified 80,049 onion services and characterized 90% of them.
arxiv.org
Public-health research measuring harmful-content availability and searches on the Tor network.
arxiv.org
CRATOR is a proposed dark web crawler described in an arXiv paper. It is designed to extract pages that handle security protocols such as captchas, using seed URL lists, link analysis and scanning to discover content, with user-agent rotation and proxy usage for anonymity. The authors report evaluation using coverage, performance and robustness metrics, and suggest threat intelligence, cybersecurity and online investigation applications.
arxiv.org
An arXiv paper measuring onion website discovery and Tor users' interests with honeypots. The authors deployed honeypot onion websites in March–April 2025 and seeded links via three channels to observe interaction events. They report that human users almost without exception originated from Ahmia.fi, and that the CSAM-themed honeypot drew markedly higher engagement than the others.
arxiv.org
This dataset maps the network layer of the Invisible Internet Project (I2P), collected using the SWARM-I2P framework, which deployed I2P routers as mapping agents. It documents over 50,000 nodes with metrics such as bandwidth, latency, and uptime, plus network traffic records and geographic distribution. Node identifiers were anonymized. Data is available in CSV and TXT formats on Zenodo, with mapping scripts on GitHub.