In recent years， underwater acoustic communication network plays a crucial role in underwater information transmission. Due to the open nature of underwater communication channels， they are more prone to attacks such as interference， tempering， and eavesdropping， so that underwater acoustic communication networks face security challenges different from traditional networks. However， traditional anomaly detection methods have lower accuracy when applied to underwater acoustic networks directly. At the same time， although machine learning-based anomaly detection methods improve accuracy， they face problems such as limited datasets and poor model interpretability. Therefore， CNN-BiLSTM integrating attention mechanism was applied for anomaly attack detection in underwater acoustic networks， and WCBA （underWater CNN-BiLSTM-Attention） model was proposed. In the model， the high dimension of dataset was reduced effectively through IG-PCA （Integrated Gradient-Principal Component Analysis） feature selection algorithm， and the identification of abnormal attacks in complex underwater data was enabled by fully utilizing the spatio-temporal features of multi-dimensional matrix acoustic network traffic. Experimental results show that WCBA model provides higher accuracy and interpretability compared to other neural network models when the dataset is limited.