Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network. Traditional methods for censoring full-packet-encryption based VPN protocols requires assuming a substantial amount of collateral damage, as other non-VPN network traffic that appears random will be blocked. I tested several machine learning-based classification models against the Aggressive Circumvention of Censorship (ACC) protocol, a fully-encrypted evasive VPN protocol which merges strategies from a wide variety of currently in-use evasive VPN protocols. My testing found that while ACC was able to survive our models when compared to random noise, it was easily detectable with minimal collateral damage using several different machine learning models when within a stream of regular network traffic. While resistant to the current techniques deployed by nation-state censors, the ACC protocol and other evasive protocols are potentially subject to packet-based protocol identification utilizing similar classification models.

翻译：全包加密是现代规避型虚拟专用网络（VPN）采用的一种技术，通过将流量伪装成网络上的随机噪声，从而避免基于协议的审查模型标记。审查基于全包加密的VPN协议的传统方法需要假设大量附带损害，因为其他看似随机的非VPN网络流量也将被阻断。本文针对"激进规避审查"（ACC）协议测试了多种基于机器学习的分类模型，该协议是一种全加密的规避型VPN协议，融合了当前多种正在使用的规避型VPN协议策略。测试发现，虽然与随机噪声相比ACC能够抵御我们的模型检测，但在常规网络流量流中，使用多种不同的机器学习模型可以轻松检测到该协议，且附带损害极小。尽管ACC协议及其他规避型协议对当前国家层面审查机构部署的技术具有抵抗性，但利用类似的分类模型进行基于数据包的协议识别仍可能对其构成威胁。