{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:51:43Z","timestamp":1782348703979,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial\u2013temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments.<\/jats:p>","DOI":"10.3390\/fi18010005","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T14:27:47Z","timestamp":1766500067000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Dual-Attention CNN\u2013GCN\u2013BiLSTM Framework for Intelligent Intrusion Detection in Wireless Sensor Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0029-0859","authenticated-orcid":false,"given":"Laith H.","family":"Baniata","sequence":"first","affiliation":[{"name":"Department of Autonomous Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8782-9721","authenticated-orcid":false,"given":"Ashraf","family":"ALDabbas","sequence":"additional","affiliation":[{"name":"Intelligent Systems Department, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2322-822X","authenticated-orcid":false,"given":"Jaffar M.","family":"Atwan","sequence":"additional","affiliation":[{"name":"Intelligent Systems Department, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"},{"name":"Department of Computer Science, Faculty of Information Technology, Applied Science Private University, Amman 11937, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4261-5842","authenticated-orcid":false,"given":"Hussein","family":"Alahmer","sequence":"additional","affiliation":[{"name":"Department of Autonomous Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2361-9781","authenticated-orcid":false,"given":"Basil","family":"Elmasri","sequence":"additional","affiliation":[{"name":"Intelligent Systems Department, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5104-1177","authenticated-orcid":false,"given":"Chayut","family":"Bunterngchit","sequence":"additional","affiliation":[{"name":"Division of Industrial and Logistics Engineering Technology, Faculty of Engineering and Technology, King Mongkut\u2019s University of Technology North Bangkok, Rayong Campus, Rayong 21120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MCAS.2005.1507522","article-title":"Wireless sensor networks: Applications and challenges of ubiquitous sensing","volume":"5","author":"Puccinelli","year":"2005","journal-title":"IEEE Circuits Syst. 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