Incorporating Deception into CyberBattleSim for Autonomous Defense
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Abstract (arXiv)
Deceptive elements, including honeypots and decoys, were incorporated into the Microsoft CyberBattleSim experimentation and research platform. The defensive capabilities of the deceptive elements were tested using reinforcement learning based attackers in the provided capture the flag environment. The attacker's progress was found to be dependent on the number and location of the deceptive elements. This is a promising step toward reproducibly testing attack and defense algorithms in a simulated enterprise network with deceptive defensive elements.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 29 references that are in the map. First posted on arXiv 31 Aug 2021.
Builds on
5 of 29 references.
References · 5
- 2017AI Safety Gridworldscited by 108
- 2019Deep Reinforcement Learning for Cyber Securitycited by 92
- 2021Network Defense is Not a Gamecited by 6
- 2018Demystifying Deception Technology:A Surveycited by 2
- 2019Strategic Learning for Active, Adaptive, and Autonomous Cyber Defensecited by 1
Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2023Automated Cyber Defence: A Reviewcited by 19
- 2023A Multiagent CyberBattleSim for RL Cyber Operation Agentscited by 8
- 2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Surveycited by 6
- 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agentscited by 6
- 2024The Path To Autonomous Cyber Defensecited by 3
- 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents19 Sep 2025
- 2024The Path To Autonomous Cyber Defense12 Apr 2024
- 2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey11 Oct 2023
- 2023A Multiagent CyberBattleSim for RL Cyber Operation Agents3 Apr 2023
- 2023Automated Cyber Defence: A Review8 Mar 2023