Machine Learning Interpretability Meets TLS Fingerprinting

TL;DR

This paper introduces a framework combining machine learning and interpretability techniques to identify the most vulnerable information fields in the TLS protocol, revealing critical leak points during data transmission.

Contribution

It presents a novel systematic approach to detect vulnerable TLS protocol fields using machine learning interpretability methods, enhancing understanding of data leakage sources.

Findings

TLS handshake is a major unencrypted leak point

TLS record length is highly indicative of sensitive data

Initialization vector (IV) field significantly leaks information

Abstract

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. Then, by employing the interpretation techniques developed in the machine learning community and applying our framework, we find the most vulnerable information fields in the TLS protocol. Our findings demonstrate that the TLS handshake (which is mainly unencrypted), the TLS record length appearing in the TLS application data header, and the initialization vector (IV) field are among the most critical leaker parts in this protocol, respectively.