Current Volume 9
Abstract
The Tor network provides anonymous communication through layered encryption and distributed relay routing. While essential for privacy protection, Tor has also been leveraged for illicit marketplaces and cybercrime coordination. Conventional surveillance approaches are ineffective due to onion routing and hidden service isolation. This paper proposes a comprehensive deanonymization framework integrating traffic metadata analysis, supervised and unsupervised machine learning, deep flow correlation techniques, and structured OpenSource Intelligence (OSINT) enrichment. The system bridges probabilistic traffic inference with contextual entity mapping using weighted evidence models. A detailed methodology covering controlled traffic acquisition, feature engineering, adversarial modeling, validation protocols, and OSINT scoring is presented. The framework emphasizes ethical compliance, reproducibility, and investigator usability. Results demonstrate that combining ML-based traffic inference with OSINT enrichment significantly improves actionable intelligence while maintaining analytical rigor.
Keywords
Tor, Deanonymization, Hidden Services, Website Fingerprinting, Flow Correlation, OSINT, Machine Learning, Cybersecurity
Citations
IRE Journals:
Falguni Sultane, Mrunali Waghdhare, Anushka Jirge, Prof. Sudhakar Yerme "De-Anonymizing Entities on Onion Sites Operating in The TOR Network" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3591-3597 https://doi.org/10.64388/IREV9I10-1716951
IEEE:
Falguni Sultane, Mrunali Waghdhare, Anushka Jirge, Prof. Sudhakar Yerme
"De-Anonymizing Entities on Onion Sites Operating in The TOR Network" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716951
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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