{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:27:24Z","timestamp":1776443244208,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Committee of science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24993166"],"award-info":[{"award-number":["BR24993166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17070301","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T03:47:26Z","timestamp":1751600846000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks"],"prefix":"10.3390","volume":"17","author":[{"given":"Kenan Honore","family":"Robacky Mbongo","sequence":"first","affiliation":[{"name":"School of Software, Henan University, Kaifeng 475001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kanwal","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Software, Henan University, Kaifeng 475001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Orken","family":"Mamyrbayev","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9275-8323","authenticated-orcid":false,"given":"Guanghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Henan University, Kaifeng 475001, China"},{"name":"Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng 475001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Software, Henan University, Kaifeng 475001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4439-7313","authenticated-orcid":false,"given":"Ainur","family":"Akhmediyarova","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurzhan","family":"Mukazhanov","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assem","family":"Ayapbergenova","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1007\/s10207-024-00833-z","article-title":"MLSTL-WSN: Machine learning-based intrusion detection using SMOTETomek in WSNs","volume":"23","author":"Talukder","year":"2024","journal-title":"Int. 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