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Model-free RL controllers enhance cyber-physical system resilience against attacks · arXiv paper

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv explores the effectiveness of model-free reinforcement learning (RL) controllers in enhancing the resilience of cyber-physical systems against cyberattacks. The study analyzes four RL reward types, finding that the Lyapunov reward offers superior resilience with minimal tracking error. Proximal Policy Optimization (PPO) demonstrated better performance than Deep Deterministic Policy Gradient (DDPG), significantly reducing key performance indicator variance. AI

IMPACT This research could lead to more robust AI-driven security for critical infrastructure and industrial control systems.

RANK_REASON The cluster contains a research paper published on arXiv detailing new findings in reinforcement learning for cyber-physical systems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hugo O. Garc\'es, Alejandro J. Rojas, Bernardo A. Hern\'andez, Andr\'es Escalona, Jonathan M. Palma, Md. Rezwan Parvez, Bhushan Gopaluni, Sirish L. Shah ·

    Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

    arXiv:2606.19069v1 Announce Type: cross Abstract: This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resili…

  2. arXiv cs.LG TIER_1 English(EN) · Sirish L. Shah ·

    Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

    This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers…

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