<![CDATA[Cyber-Physical Systems (CPS) play a crucial role in critical industries such as manufacturing, transportation, energy, and healthcare by integrating the physical and digital worlds. However, the complexity and interconnectivity of CPS with the global network increase their vulnerability to cyber-attacks. This research explores the benefits of implementing artificial intelligence (AI) in the context of cyber-physical systems (CPS) to detect and respond to security threats. This study uses machine learning and deep learning techniques to analyze sensor data and system threats. The data analysis methods encompass predictive modeling and evaluating AI algorithms' performance in detecting threats. The research data is obtained from relevant literature reviews and secondary data analysis. The research findings indicate that integrating AI in CPS can enhance the success rate of threat detection, prompt response, and accuracy in threat identification. By enabling the system to learn from previous experiences, AI can reduce the number of false positives and false negatives while providing automated real-time responses to threats without human intervention. The research concludes that AI has great potential to enhance security in CPS by providing more efficient and effective solutions to address increasingly complex cyber threats. The findings of this study are expected to provide insights and recommendations that can be applied in developing CPS security systems in the future.]]>
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