Implementation of Network Traffic Anomaly Detection Based on Autoencoder

Due to the increasing complexity and diversity of network traffic patterns, it is difficult for traditional detection methods to effectively identify new or unknown attacks, which has triggered the need for deep learning-based anomaly detection research. Based on the existing Autoencoder model, this study constructs a complex network traffic anomaly detection framework combining multi-layer nested autoencoder and attention mechanism. Firstly, the potential representation of high-dimensional network traffic data is extracted through layer-by-layer pre-training and reconstruction of multi-layer autoencoder. Then, attention modules are introduced between the encoders of each layer to emphasize the key features related to cyber attacks and suppress the noise characteristics. At the same time, a trainable threshold optimization module is added to the decoding end to adapt to different types of attack scenarios by dynamically adjusting the abnormal score boundary. Finally, multiple regularization strategies and adaptive learning rate methods are integrated into the training process to ensure that the model maintains high efficiency, robustness and generalization ability in a variety of complex network environments. Experimental results show that the detection accuracy and robustness of the model are significantly better than those of mainstream network traffic anomaly detection methods.