Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks

Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev (), Guanghui Wang (), Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov and Assem Ayapbergenova
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Kenan Honore Robacky Mbongo: School of Software, Henan University, Kaifeng 475001, China
Kanwal Ahmed: School of Software, Henan University, Kaifeng 475001, China
Orken Mamyrbayev: Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan
Guanghui Wang: School of Software, Henan University, Kaifeng 475001, China
Fang Zuo: School of Software, Henan University, Kaifeng 475001, China
Ainur Akhmediyarova: Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan
Nurzhan Mukazhanov: Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan
Assem Ayapbergenova: Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan

Future Internet, 2025, vol. 17, issue 7, 1-20

Abstract: 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.

Keywords: intrusion detection; Deep Neural Networks (DNNs); Convolutional Neural Networks (CNNs); Gated Recurrent Units (GRU); Self-Attention mechanisms (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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