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.

翻译：本研究探讨通过被动流量分析与机器学习技术对隐形互联网计划（I2P）网络中的服务进行去匿名化的可能性。主要目标是在I2P流量载荷加密的情况下识别其独特模式。为此，建立了受控实验室环境以生成合成I2P流量，为机器学习模型提供训练数据集。此外，利用Fano不等式对I2P等混合网络中的匿名数据传输进行理论分析，从而支持基于数据驱动的方法揭示因果关系。在计算机实验中，将先进的深度学习方法——特别是卷积神经网络——应用于实验室I2P网络，并通过真实世界流量数据进一步评估其有效性。结果表明，所提出的方法并未削弱I2P网络的匿名性保障。