{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:19:51Z","timestamp":1781194791700,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Researchers Supporting Project","award":["RSPD2024R636"],"award-info":[{"award-number":["RSPD2024R636"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.<\/jats:p>","DOI":"10.3390\/s24185933","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T03:03:37Z","timestamp":1726196617000},"page":"5933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4081-0553","authenticated-orcid":false,"given":"Engy","family":"El-Shafeiy","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computers & Artificial Intelligence, University of Sadat City, Sadat City 32897, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-902X","authenticated-orcid":false,"given":"Walaa M.","family":"Elsayed","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers & Information Systems, Damanhour University, Damanhour 22511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0920-2445","authenticated-orcid":false,"given":"Haitham","family":"Elwahsh","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8601-3184","authenticated-orcid":false,"given":"Maazen","family":"Alsabaan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-4161","authenticated-orcid":false,"given":"Mohamed I.","family":"Ibrahem","sequence":"additional","affiliation":[{"name":"School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gamal Farouk","family":"Elhady","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","unstructured":"Dahlqvist, F., Patel, M., Rajko, A., and Shulman, J. 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