CLOUD IOT ENVIRONMENTS SECURITY: DEEP LEARNING WITH GENERATIVE AND EXPLAINABLE MODELS

IoT devices have grown exponentially, and businesses are utilizing cloud computing to integrate complex applications with those devices, which poses excellent security concerns regarding confidentiality, integrity, and availability of sensitive data. This paper offers a security framework to mitigate against the threats above utilizing deep learning, Generative Adversarial Networks (GANs), and Explainable AI (XAI). To alleviate the challenges of anomaly detection, especially for rare and novel attacks, the proposed framework uses GANs (Generative Adversarial Networks) to produce synthetic data. XAI methods such as SHAP and LIME have been established to bring more transparency and trust to models, which is incredibly important for security professionals. The framework also integrates federated learning, where models can be trained across decentralized devices while keeping data private. The experimental results demonstrate that our proposed model can achieve achieving94.8% accuracy94.6%precision97.1%recallon NSL-KDD datasets, which are lower than other models Lowest Latency of 45ms per sample. The results validate the model for scalable, interpretable, and privacy-preserving real-time Internet of Things (IoT) security applications.