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，网络攻击者监控和审查互联网流量正变得越来越困难。流量分析成为攻击者仍可利用的一种途径：即通过分析加密流量的模式来推断用户及其活动信息。近期基于深度学习的改进使得流量分析攻击比以往任何时候都更加有效。本文提出Maybenot，一种流量分析防御框架。该框架设计旨在易于使用并集成到现有的端到端加密协议中。我们以Rust编程语言的crate（库）形式实现了该框架，并配备模拟器以促进防御机制的进一步发展。Maybenot中的防御机制通过概率状态机实现，可调度数据包填充或阻断外发流量等操作。该框架是对Perry与Kadianakis提出的Tor电路填充框架的演进，旨在支持更广泛的协议和应用场景。