A Dual-Attention CNN–GCN–BiLSTM Framework for Intelligent Intrusion Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. The traditional intrusion detection systems (IDS) in WSNs are based on machine learning techniques. Such models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. Consequently, they cause degradation in the detection accuracy and poor adaptability against evolving threats. To overcome these limitations, this study introduced a hybrid deep learning-based IDS that integrated multi-scale convolutional feature extraction, dualstage attention fusion, and graph convolutional reasoning. In addition, bidirectional long short-term memory components are embedded into the unified framework. The proposed architecture captures the hierarchical spatial-temporal correlations in the traffic patterns. This allows making a precise discrimination between the normal and attack behaviors across several intrusion classes. The model has been evaluated on the benchmarking public available dataset and found to attain a higher classification capability in the multiclass scenarios. The model has further been found to outperform the conventional models focusing on the IDS frameworks. In addition, the proposed design is aimed at retaining suitable computational efficiency, which is suitable for edge and distributed deployments. This makes it an effective solution for the next-generation WSN cybersecurity. The overall findings have focused on combining topology-aware learning with multi-branch attention mechanisms for offering a balanced trade-off between interpretability, accuracy, and deployment efficiency for the resource-constrained WSN networks.