Recognizing Anomalies in Cyber-Physical Systems using Reinforcement Learning
Keywords:
Cyber-Physical Systems, Anomaly Detection, Reinforcement Learning, Q-Learning, Industrial AutomationAbstract
CPS connect networking, control, computing, and physics. Industrial automation, smart grids, and autonomous automobiles are increasingly prone to anomalies that might trigger system failures or security breaches. The frequent variation of CPS makes traditional anomaly detection challenging. This study proposes proactive CPS anomaly detection using reinforcement learning (RL). Learning from ambient interactions, RL can identify irregularities in real time and avoid future problems to increase system reliability and security. Use Q-learning, DQN, and multi-agent reinforcement learning to find CPS anomalies. Large training data and computational complexity hinder CPS RL deployment. Finally, case studies and CPS anomaly detection system RL integration are examined.
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