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The rapid proliferation of Internet of Things (IoT) devices has revolutionized various domains, introducing unprecedented convenience and efficiency. However, this expansion has concurrently intensified the obstacles associated with network security. Detecting and mitigating anomalies in IoT networks are of utmost importance to protect critical systems and sensitive data. This paper presents an innovative approach to IoT network security that combines Principal Component Analysis (PCA) with advanced deep learning approaches, encompassing Generative Adversarial Networks (GANs) and Transformers. In this research, we investigate the potential of PCA for dimensionality reduction and feature selection within IoT network data. By simplifying data complexity, PCA aids in uncovering critical features and patterns contributing to network anomalies. Advanced deep learning models, such as GANs for generating …