Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

TL;DR

This paper explores using convolutional neural networks and theoretical analysis to deanonymize I2P traffic, assessing the effectiveness and privacy implications of such passive traffic analysis techniques.

Contribution

It introduces a combined empirical and theoretical approach using deep learning and Fano's inequality to analyze I2P traffic deanonymization risks.

Findings

Deep learning methods can identify patterns in I2P traffic without breaking anonymity.

Theoretical analysis suggests limits to deanonymization based on information theory.

Experimental results show the approach does not compromise I2P's anonymity guarantees.

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.