机器学习可解释性满足TLS指纹识别要求
Machine Learning Interpretability Meets TLS Fingerprinting
摘要：
保护用户在互联网上的隐私是非常重要的;然而，由于网络协议和组件的日益复杂，它变得越来越难以维护。因此，调查和了解数据是如何从信息传输平台和协议中泄露的，可以帮助我们建立一个更安全的环境。在本文中，我们提出了一个框架，系统地找到最脆弱的信息领域的网络协议。为此，我们以传输层安全（TLS）协议为重点，对来自70多个域（网站）的收集数据进行不同的基于机器学习的指纹攻击，以了解TLS协议中这种信息泄漏是如何以及在何处发生的。然后，通过采用机器学习社区开发的解释技术，并应用我们的框架，我们找到了TLS协议中最脆弱的信息字段。我们的研究结果表明，TLS握手（主要是未加密的），TLS应用程序数据头中出现的TLS记录长度和初始化向量（IV）字段分别是该协议中最关键的泄漏部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
