{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:52:40Z","timestamp":1775145160894,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council (NSTC)","award":["NSTC112-2221-E-468-008-MY3"],"award-info":[{"award-number":["NSTC112-2221-E-468-008-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model\u2019s proficiency in distinguishing various attack categories, including \u2018Normal\u2019, \u2018DoS\u2019 (Denial of Service), \u2018Probe\u2019, \u2018U2R\u2019 (User to Root), and \u2018Sybil\u2019. Additionally, the confusion matrix offers valuable insights into the model\u2019s performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model\u2019s robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure<\/jats:p>","DOI":"10.3390\/s23218686","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T11:39:04Z","timestamp":1698147544000},"page":"8686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities"],"prefix":"10.3390","volume":"23","author":[{"given":"Brij B.","family":"Gupta\u00a0","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan"},{"name":"Center for Advanced Information Technology, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea"},{"name":"Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune 412115, India"},{"name":"School of Computing, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates"},{"name":"Department of Electrical and Computer Engineering, Lebanese American University, Beirut 1102, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-9901","authenticated-orcid":false,"given":"Kwok Tai","family":"Chui","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University (HKMU), Hong Kong"}]},{"given":"Akshat","family":"Gaurav\u00a0","sequence":"additional","affiliation":[{"name":"Ronin Institute, Montclair, NJ 07043, USA"}]},{"given":"Varsha","family":"Arya\u00a0","sequence":"additional","affiliation":[{"name":"Department of Business Administration, Asia University, Taichung 413, Taiwan"},{"name":"Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India"},{"name":"Chandigarh University, Chandigarh 140413, India"}]},{"given":"Priyanka","family":"Chaurasia\u00a0","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Londonderry BT48 7JL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSWIS.306260","article-title":"Adaptive Ontology-Based IoT Resource Provisioning in Computing Systems","volume":"18","author":"Tiwari","year":"2022","journal-title":"Int. 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