Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning

Abstract

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security.