Research on AI-based Anomaly Detection and Diagnosis Algorithms for IoT Devices

This paper aims to explore an Artificial Intelligence (AI)-based anomaly detection and diagnosis method for the increasing complexity and diversity of Internet of Things (IoT) devices. In the study, a real device monitoring dataset was first collected, and experimental data were prepared through preprocessing and annotation work. Subsequently, an anomaly detection model based on Generative Adversarial Networks (GANs) and a data repair method based on transfer learning were constructed and integrated into a comprehensive model. The proposed method was comprehensively validated through simulation experiments. The results show that the proposed algorithm performs excellently in anomaly detection accuracy, achieving an accuracy of over 97% with extremely low errors. This method also demonstrates efficient processing speed. The algorithm also demonstrates excellent stability, maintaining relatively stable performance when dealing with complex and variable device monitoring data. In summary, the algorithm proposed in this paper provides a new approach for anomaly detection and diagnosis of IoT devices, with certain application prospects and practical value.