Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and Defenses Article Swipe
Aritran Piplai
,
Mike Anoruo
,
Kayode Fasaye
,
Anupam Joshi
,
Tim Finin
,
Ahmad Ridley
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icmla55696.2022.00213
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icmla55696.2022.00213
Cyber defense exercises are an important avenue to understand the technical capacity of organizations when faced with cyber-threats. Information derived from these exercises often leads to finding unseen methods to exploit vulnerabilities in an organization. These often lead to better defense mechanisms that can counter previously unknown exploits. With recent developments in cyber battle simulation platforms, we can generate a defense exercise environment and train reinforcement learning (RL) based autonomous agents to attack the system described by the simulated environment. In this paper, we describe a two-player game-based RL environment that simultaneously improves the performance of both the attacker and defender agents. We further accelerate the convergence of the RL agents by guiding them with expert knowledge from Cybersecurity Knowledge Graphs on attack and mitigation steps. We have implemented and integrated our proposed approaches into the CyberBattleSim system.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icmla55696.2022.00213
- OA Status
- green
- Cited By
- 10
- References
- 40
- Related Works
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- OpenAlex ID
- https://openalex.org/W4360764596
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4360764596Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icmla55696.2022.00213Digital Object Identifier
- Title
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Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and DefensesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-12-01Full publication date if available
- Authors
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Aritran Piplai, Mike Anoruo, Kayode Fasaye, Anupam Joshi, Tim Finin, Ahmad RidleyList of authors in order
- Landing page
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https://doi.org/10.1109/icmla55696.2022.00213Publisher landing page
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://hdl.handle.net/11603/26478Direct OA link when available
- Concepts
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Exploit, Reinforcement learning, Computer science, Computer security, Convergence (economics), Battle, Human–computer interaction, Artificial intelligence, History, Economics, Archaeology, Economic growthTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 7, 2023: 2Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2952298682, https://openalex.org/W4239319433, https://openalex.org/W3000539293, https://openalex.org/W2997634552, https://openalex.org/W6744241167, https://openalex.org/W6790796223, https://openalex.org/W3179788886, https://openalex.org/W3112693483, https://openalex.org/W2592160065, https://openalex.org/W2126599452, https://openalex.org/W2308978510, https://openalex.org/W1975385576, https://openalex.org/W4286284483, https://openalex.org/W2913497771, https://openalex.org/W2900633536, https://openalex.org/W2762155482, https://openalex.org/W3037857795, https://openalex.org/W3137726751, https://openalex.org/W6759896672, https://openalex.org/W6802879114, https://openalex.org/W2964061570, https://openalex.org/W1509474702, https://openalex.org/W2970023415, https://openalex.org/W6757677476, https://openalex.org/W2149950914, https://openalex.org/W1996006421, https://openalex.org/W6754047560, https://openalex.org/W1993842826, https://openalex.org/W6793544917, https://openalex.org/W6800884497, https://openalex.org/W6661564250, https://openalex.org/W2067429903, https://openalex.org/W2145339207, https://openalex.org/W2487152776, https://openalex.org/W2997993688, https://openalex.org/W4287022721, https://openalex.org/W3127446549, https://openalex.org/W3154457404, https://openalex.org/W2753718471, https://openalex.org/W3207252194 |
| referenced_works_count | 40 |
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