End-to-end encryption is a powerful tool for protecting the privacy of Internet users. Together with the increasing use of technologies such as Tor, VPNs, and encrypted messaging, it is becoming increasingly difficult for network adversaries to monitor and censor Internet traffic. One remaining avenue for adversaries is traffic analysis: the analysis of patterns in encrypted traffic to infer information about the users and their activities. Recent improvements using deep learning have made traffic analysis attacks more effective than ever before. We present Maybenot, a framework for traffic analysis defenses. Maybenot is designed to be easy to use and integrate into existing end-to-end encrypted protocols. It is implemented in the Rust programming language as a crate (library), together with a simulator to further the development of defenses. Defenses in Maybenot are expressed as probabilistic state machines that schedule actions to inject padding or block outgoing traffic. Maybenot is an evolution from the Tor Circuit Padding Framework by Perry and Kadianakis, designed to support a wide range of protocols and use cases.

翻译：端到端加密是保护互联网用户隐私的有力工具。随着Tor、VPN和加密消息等技术的日益普及，网络对手监控和审查互联网流量正变得越来越困难。对手仍可利用的一个途径是流量分析：通过分析加密流量中的模式来推断用户及其活动的信息。近期利用深度学习的改进已使流量分析攻击比以往任何时候都更加有效。我们提出了Maybenot，一个用于流量分析防御的框架。Maybenot的设计目标是易于使用，并能集成到现有的端到端加密协议中。它采用Rust编程语言实现为crate（库），并附带一个模拟器以促进防御方案的开发。Maybenot中的防御方案被表示为概率状态机，这些状态机调度操作以注入填充或阻止传出流量。Maybenot是Perry和Kadianakis提出的Tor电路填充框架的演变版本，旨在支持广泛的协议和用例。