Machine Learning Interpretability Meets TLS Fingerprinting

Understand

Protecting users' privacy over the Internet is of great importance; however, it becomes harder and harder to maintain due to the increasing complexity of network protocols and components.

• Therefore, investigating and understanding how data is leaked from the information transmission platforms and protocols can lead us to a more secure environment.
• In this paper, we propose a framework to systematically find the most vulnerable information fields in a network protocol.
• To this end, focusing on the transport layer security (TLS) protocol, we perform different machine-learning-based fingerprinting attacks on the collected data from more than 70 domains (websites) to understand how and where this information leakage occurs in the TLS protocol.