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.

The increasing complexity and frequency of cyberattacks have made accurate and reliable intrusion detection systems (IDSs) essential for modern network security. In this study, an explainable triple-hybrid deep learning framework is proposed for binary intrusion detection using the CICIDS2017 dataset. The proposed architecture integrates three complementary branches, namely a Transformer encoder, a bidirectional long short-term memory (BiLSTM) network, and a multilayer perceptron (MLP), to capture global feature interactions, sequential dependencies, and nonlinear discriminative patterns from network traffic data. To enhance adaptive representation learning, the framework employs a branch-gating mechanism and a fusion-gating module before final classification. The model was evaluated in a Benign-versus-Attack setting using 5-fold stratified cross-validation and assessed through accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, and explainability analysis. Experimental results showed strong and stable performance across folds, with a mean validation accuracy of 97.18%, a best-fold accuracy of 97.43%, and a mean ROC-AUC of 0.9975. LIME-based explanations further improved transparency, confirming the framework as an effective and interpretable solution for binary intrusion detection.