mlsanityData to 7 Oct 2026

Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning

arXiv 2402.09200 ↗First posted 14 Feb 2024cs.CR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 14 Feb 2024.

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14 of 40 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  5. 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 260
  6. 2019Deep Reinforcement Learning for Cyber Securitycited by 92
  7. 2021Deep hierarchical reinforcement agents for automated penetration testingcited by 19
  8. 2021Using Cyber Terrain in Reinforcement Learning for Penetration Testingcited by 16
  9. 2021Crown Jewels Analysis using Reinforcement Learning with Attack Graphscited by 8
  10. 2022Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphscited by 8
  11. 2022A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphscited by 4
  12. 2022Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terraincited by 3
2 more
  1. 2024Discovering Command and Control Channels Using Reinforcement Learningcited by 3
  2. 2014Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defencescited by 1

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