Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security
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Abstract (arXiv)
Cyber-Physical Systems play a critical role in the infrastructure of various sectors, including manufacturing, energy distribution, and autonomous transportation systems. However, their increasing connectivity renders them highly vulnerable to sophisticated cyber threats, such as adaptive and zero-day attacks, against which traditional security methods like rule-based intrusion detection and single-agent reinforcement learning prove insufficient. To overcome these challenges, this paper introduces a novel Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning (HAMARL) framework. HAMARL employs a hierarchical structure consisting of local agents dedicated to subsystem security and a global coordinator that oversees and optimizes comprehensive, system-wide defense strategies. Furthermore, the framework incorporates an adversarial training loop designed to simulate and anticipate evolving cyber threats, enabling proactive defense adaptation. Extensive experimental evaluations conducted on a simulated industrial IoT testbed indicate that HAMARL substantially outperforms traditional multi-agent reinforcement learning approaches, significantly improving attack detection accuracy, reducing response times, and ensuring operational continuity. The results underscore the effectiveness of combining hierarchical multi-agent coordination with adversarially-aware training to enhance the resilience and security of next-generation CPS.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 12 Jun 2025.
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19 of 35 references.
References · 19
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2017Graph Attention Networkscited by 8,923
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
- 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
- 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 286
- 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
- 2023Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Gamecited by 18
- 2024Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimizationcited by 15
- 2023Robust Adversarial Reinforcement Learning via Bounded Rationality Curriculacited by 10
7 more
- 2023Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learningcited by 10
- 2023Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RLcited by 7
- 2024SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systemscited by 7
- 2018Internet of Things Security and Forensics: Challenges and Opportunitiescited by 5
- 2024Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationingcited by 4
- 2024Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planningcited by 4
- 2023Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learningcited by 1
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