Wavelet and Dynamic Convolutional Attention-Based Anomaly Detection for 6G IoT Security

With the development of Sixth Generation (6G) Internet of Things (IoT) technology, ensuring data reliability and security in networks has become a critical issue. To address the identification of abnormal behaviors in network traffic, this study proposes an anomaly traffic detection algorithm combining wavelet analysis and machine learning. By utilizing wavelet analysis, this paper ex-tracts time-frequency features from Fifth Generation (5G) core network traffic data, which effectively capture abrupt changes and periodic fluctuations in the data. Combining deep learning models, particularly dynamic convolution and attention mechanisms, this method adaptively optimizes the feature extraction process, enhancing the model’s sensitivity and accuracy in detecting key traffic features. Experimental results demonstrate that the proposed algorithm outperforms traditional methods in multiple standard datasets, with superior performance in accuracy, precision, recall, and other evaluation metrics.