FD3RN: Fused Weight–Based Hybrid Distributed Drift‐Enabled Deep Learning Model for Malicious Node Detection in Wireless Sensor Network

Wireless sensor networks (WSNs) consist of lightweight, low‐power processing units known as sensor nodes, which communicate wirelessly via radio transceivers to form an interconnected network. However, there is a possibility of the malfunctioning of such sensor nodes in the network, thereby resulting in security concerns. The earlier attack detection frameworks exhibit certain discrepancies in terms of reduced detection accuracy, increased false alarm rate, and higher execution timings. Therefore, the research proposes an efficient fused weight tuning–based hybrid Distributed Drift‐enabled Deep Recurrent neural Network (FD3RN) to perform malicious node detection. The drift mechanism enables the model to offer optimal performance with reduced false positives, irrespective of the dynamic data characteristics. Furthermore, the weight updation using the fused adam gradient descent (FAGD) optimization improves the robustness of the research with better adaptability. In addition, the usage of the gated attention strategy aids in the better prioritization of the essential features, thereby reducing the overfitting challenges. Moreover, the synthetic minority oversampling technique (SMOTE) mitigates the consequences of using the imbalanced data in terms of reduced computational complexity. The results underscore the advantage of using the proposed model in malicious node detection through attaining 98.12% accuracy, 98.86% precision, 97.39% recall, and 98.12% F1‐score for 90% training using the UNSW‐NB15 dataset.