基于深度强化学习的数字孪生自动隐身磨损攻击
Automated Stealthy Wear-Out Attack on Digital Twins With Deep Reinforcement Learning
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中文总结 AI 辅助
研究利用深度强化学习对数字孪生进行隐身磨损攻击，通过操纵控制信号加速特定关节磨损并躲避检测。测试多种算法发现SAC性能最佳，在工业环境中用UR10e机器人手臂评估，证明攻击有效，强调了该攻击对数字孪生环境的风险及防御需求。
中文摘要 AI 辅助
数字孪生已成为工业4.0的关键推动者，能实现实时监测、高级模拟和对物理资产的精确控制。但这种无缝交互也扩大了工业系统的攻击面。本文提出一种利用深度强化学习的新型隐身磨损攻击，对手通过操纵控制信号使特定关节扭矩增加以加速磨损，同时躲避异常检测系统。对多种强化学习算法测试发现SAC性能最佳。在工业环境中用UR10e机器人手臂评估，结果表明攻击能显著提高目标关节扭矩，加速退化并增加维护成本，且能隐身躲避检测。研究强调了深度强化学习驱动的对手对数字孪生环境构成的重大风险及强大防御机制的必要性。
英文摘要
Digital Twins (DTs) have emerged as pivotal enablers of Industry 4.0, offering transformative capabilities such as real-time monitoring, advanced simulation, and precise control of physical assets. By bridging the physical and virtual domains, DTs facilitate seamless integration of data-driven decision-making and operational optimisation. However, this seamless interaction significantly expands the attack surface of industrial systems, creating vulnerabilities that adversaries can exploit. This paper introduces a novel and stealthy wear-out attack leveraging Deep Reinforcement Learning (DRL) to target DT-enabled infrastructures. The adversary strategically and covertly manipulates control signals, inducing increased torque on a specific joint to accelerate wear and tear while evading detection by a state-of-the-art anomaly detection system. Extensive benchmarking of reinforcement learning algorithms - including Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic (SAC), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) - revealed that SAC consistently outperformed its counterparts in terms of sample efficiency, stability, and overall attack effectiveness. We evaluate the proposed adversary in an industrial setting using the UR10e robotic arm. Results demonstrate the adversary's ability to significantly elevate torque levels on the targeted joint, leading to accelerated degradation and increased maintenance costs, all while operating stealthily and avoiding detection. Our findings highlight the substantial risks posed by DRL-driven adversaries to DT-enabled environments and emphasise the critical need for robust defence mechanisms to protect critical industrial systems.
