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