An AI-driven cybersecurity framework for smart solar energy systems intrusion detection and adaptive threat mitigation

Abstract

The rise of Internet of Things (IoT) technologies in smart solar energy systems has presented a serious cybersecurity issue, especially with the close interdependence between cyber infrastructure and physical functions. Traditional intrusion detection systems (IDS) are mainly based on the network level of abnormalities and do not consider the system level effects leading to poor mitigation. In this paper, we suggest an AI-based cyber-physical cybersecurity architecture, which combines an intrusion detection model in the form of CNNLSTM and a mitigation mechanism based on reinforcement learning. A decision fusion layer is added to integrate cyber threats scores with photovoltaic system conditions to respond in context. Experimental analysis on real-world data sets indicates that the framework proposed has a 98.3% detection rate, 0.97 F1-score, and a lower false positive of 0.02, which performs better than the current approaches by up to 2.1% accuracy. The framework enhances energy stability at the system level up to 96.8% and recovery time is shortened by about 58 percent when the system is under attack. Moreover, the model has a strong performance of more than 96% accuracy even when there is a high attack intensity situation. These results highlight the value of cyber intelligence and integration of physical system awareness. The proposed framework is holistic and realistic to provide a guarantee of the safety of smart solar energy systems to ensure not only high detection but also stability of the operations in real-life scenarios.