基于强化学习的Tor与公共网络中命令与控制（C2）信道发现
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning
AI总结：
针对网络攻击中C2信道识别依赖人工、门槛高的问题，提出基于强化学习的方法，在公共网与Tor网中模拟攻击，可自动发现能绕过防火墙的多信道韧性C2攻击路径。
摘要：
命令与控制（C2）信道是多种网络攻击的核心组件，攻击者可通过它远程控制受恶意软件感染的设备并执行有害操作，例如在网络中传播恶意代码、窃取机密数据或发起分布式拒绝服务（DDoS）攻击。因此，识别这类C2信道对缓解和防范网络攻击至关重要。但目前C2信道识别通常依赖人工流程，需要具备深厚的网络作战知识与专业技能。本文提出一种基于强化学习（RL）的方法，可在普通（公共）网络与Tor网络中自动模拟C2攻击活动，同时配置了载荷大小与网络防火墙以模拟真实攻击场景。在典型网络配置下的实验结果表明，RL智能体能够自动发现兼具韧性的C2攻击路径，同时利用基于Tor的通信信道与传统通信信道，并且可以绕过网络防火墙。
英文摘要：
Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execute harmful actions, such as propagating malicious code across networks, exfiltrating confidential data, or initiating distributed denial of service (DDoS) attacks. Identifying these C2 channels is therefore crucial in helping to mitigate and prevent cyber attacks. However, identifying C2 channels typically involves a manual process, requiring deep knowledge and expertise in cyber operations. In this paper, we propose a reinforcement learning (RL) based approach to automatically emulate C2 attack campaigns using both the normal (public) and the Tor networks. In addition, payload size and network firewalls are configured to simulate real-world attack scenarios. Results on a typical network configuration show that the RL agent can automatically discover resilient C2 attack paths utilizing both Tor-based and conventional communication channels, while also bypassing network firewalls.
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。
