Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks
Robacky Mbongo K.H. Ahmed K. Mamyrbayev O. Wang G. Zuo F. Akhmediyarova A. Mukazhanov N. Ayapbergenova A.
July 2025Multidisciplinary Digital Publishing Institute (MDPI)
Future Internet
2025#17Issue 7
Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objective of this research is to design and evaluate a deep learning-based Intrusion Detection System (IDS) that is both accurate and efficient for real-time threat detection in WSNs. This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. The model was evaluated using the WSN-DS dataset and demonstrated superior performance compared to traditional machine learning and simpler deep learning models. It achieved an accuracy of 98.6%, precision of 98.63%, recall of 98.6%, F1-score of 98.6%, and an ROC-AUC of 0.9994, indicating strong predictive capability even with imbalanced data. In addition to centralized training, the model was tested under cooperative, node-based learning conditions, where each node independently detects anomalies and contributes to a collective decision-making framework. This distributed approach improves detection efficiency and robustness. The proposed IDS offers a scalable and resilient solution tailored to the unique challenges of WSN security.
Convolutional Neural Networks (CNNs) , Deep Neural Networks (DNNs) , Gated Recurrent Units (GRU) , intrusion detection , Self-Attention mechanisms
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School of Software, Henan University, Kaifeng, 475001, China
Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, 475001, China
Institute of Automation and Information Technologies, Satbayev University, Almaty, 050013, Kazakhstan
School of Software
Institute of Information and Computational Technologies
Henan International Joint Laboratory of Intelligent Network Theory and Key Technology
Institute of Automation and Information Technologies
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