Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

Computer Science > Cryptography and Security [Submitted on 12 May 2026] Convolutional-Neural-Networks for Deanonymisation of I2P Traffic Luca Rohrer, Konrad Baechler, Dieter Arnold This study investigates the potential for deanonymizing services within the Invisible Internet Project (I2P) network through passive traffic analysis and machine learning techniques. The primary objective is to identify distinctive patterns in I2P traffic despite the encryption of its payload. To achieve this, a controlled laboratory environment was established to generate synthetic I2P traffic, providing a training dataset for machine learning models. Furthermore, Fano's inequality is employed to perform a theoretical analysis of anonymous data transmission in mix networks such as I2P, thereby supporting a data-driven approach to uncover causal relationships. In computer experiments, advanced deep learning methods - particularly Convolutional Neural Networks - are applied within the laboratory I2P network, and their effectiveness is further evaluated using real-world traffic data. The results indicate that the proposed methodologies do not compromise the anonymity guarantees of the I2P network. Comments: 29 pages, 13 figures, 9 tables Subjects: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2605.11606 [cs.CR]   (or arXiv:2605.11606v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.11606 Focus to learn more Submission history From: Konrad Baechler [view email] [v1] Tue, 12 May 2026 06:35:37 UTC (4,435 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)