EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR SECURE CLOUD COMPUTING: A HYBRID MACHINE LEARNING FRAMEWORK

Cloud computing has become the foundation of modern digital infrastructure by providing scalable storage, computational resources, and software services. However, the rapid adoption of cloud environments has introduced sophisticated cyber threats that challenge conventional security mechanisms. Artificial Intelligence (AI) has emerged as an effective solution for detecting malicious activities, but many AI-based security systems operate as black boxes, making their decisions difficult to interpret. Explainable Artificial Intelligence (XAI) addresses this limitation by improving transparency and trust in AI models. This research proposes a hybrid machine learning framework that integrates explainable AI techniques into cloud intrusion detection systems. The proposed model combines Random Forest, Extreme Gradient Boosting (XGBoost), and SHAP (SHapley Additive Explanations) to detect and explain network attacks. Experimental evaluation demonstrates that the framework achieves high detection accuracy while providing understandable explanations for security analysts. The findings indicate that explainable AI significantly enhances cloud security management by improving decision transparency without compromising prediction performance.