mlsanityData to 7 Oct 2026

Incorporating Deception into CyberBattleSim for Autonomous Defense

arXiv 2108.13980 ↗First posted 31 Aug 2021cs.CR cs.AIcited by 5

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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

  1. 2017AI Safety Gridworldscited by 108
  2. 2019Deep Reinforcement Learning for Cyber Securitycited by 92
  3. 2021Network Defense is Not a Gamecited by 6
  4. 2018Demystifying Deception Technology:A Surveycited by 2
  5. 2019Strategic Learning for Active, Adaptive, and Autonomous Cyber Defensecited by 1

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Citing papers per year: 2023: 3; 2024: 1; 2025: 1.

5 papers.