Bidirectional Temporal Attention Convolutional Networks for High-Performance Network Traffic Anomaly Detection

Deep learning-based network traffic anomaly detection, particularly using Recurrent Neural Networks (RNNs), often struggles with high computational overhead and difficulties in capturing long-range temporal dependencies. To address these limitations, this paper proposes a Bidirectional Temporal Attention Convolutional Network (Bi-TACN) for robust and efficient network traffic anomaly detection. Specifically, dilated causal convolutions with expanding receptive fields and residual modules are employed to capture multi-scale temporal patterns while effectively mitigating the vanishing gradient. Furthermore, a bidirectional structure integrated with Efficient Channel Attention (ECA) is designed to adaptively weight contextual features, preventing sparse attack indicators from being overwhelmed by dominant normal traffic. A Softmax-based classifier then leverages these refined representations to execute high-performance anomaly detection. Extensive experiments on the NSL-KDD and UNSW-NB15 datasets demonstrate that Bi-TACN achieves average accuracies of 88.51% and 82.5%, respectively, significantly outperforming baseline models such as Bi-TCN and Bi-GRU in terms of both precision and convergence speed.