Convolutional-Neural-Networks for Deanonymisation of I2P Traffic
Authors: Luca Rohrer, Konrad Baechler, Dieter Arnold
Summary
The authors investigate whether passive traffic analysis and convolutional neural networks can de-anonymize services in the I2P (Invisible Internet Project) anonymity network, despite encrypted payloads. They generated synthetic I2P traffic in a controlled lab, trained CNNs to identify distinctive patterns, and applied Fano's inequality to theoretically analyze anonymous data transmission in mix networks. Computer experiments in the lab and evaluation on real-world I2P traffic show that the proposed methods do not compromise I2P's anonymity guarantees.
Main takeaways:
- Goal: identify I2P services via passive traffic analysis (timing, packet sizes) even though payloads are encrypted
- Generated synthetic I2P traffic in a lab environment as a training dataset for CNNs
- Used Fano's inequality for theoretical analysis of information leakage in mix networks like I2P
- Evaluated CNNs in the lab I2P network and on real-world traffic
- Results indicate the proposed methods do not break I2P anonymity—distinguishing patterns were insufficient
Relevance
Not related to my persona or midtraining research. Included because it's a useful negative result for privacy/anonymity evaluation, but no obvious connection to LLM behavior installation.
Abstract
arXiv:2605.11606v1 Announce Type: new Abstract: 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.