🤖 AI Summary
This study investigates the feasibility of de-anonymizing users in the I2P network through passive traffic analysis. To address potential unique patterns in encrypted traffic, the authors generate synthetic I2P traffic in a controlled environment and, for the first time, integrate convolutional neural networks with Fano’s inequality from information theory to data-drivenly uncover causal relationships among traffic flows and assess the de-anonymization capability of deep learning models on real I2P traffic. Experimental results demonstrate that the proposed approach fails to breach the anonymity guarantees provided by I2P under its current configuration, thereby empirically validating the effectiveness of its anonymity mechanisms. Furthermore, this work introduces a novel methodology for analyzing the theoretical limits of anonymous communication networks.
📝 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.