{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:36:57Z","timestamp":1770226617342,"version":"3.49.0"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"crossref","award":["PNURSP2026R508"],"award-info":[{"award-number":["PNURSP2026R508"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>Current developments on the Internet of Things (IoT) have completely changed the landscape of Consumer Electronics (CE), enabling next-generation gadgets to be highly intelligent, networked and capable of utilizing large models. In addition to improving data accessibility, this improved connectivity between sensors, actuators and appliances allows autonomous control inside CE networks. Although deep learning models are highly effective in analyzing large amounts of data, controlling the intricacy of deep learning-based intrusion detection in low-latency IoT networks continues to be a formidable task. This paper presents a deep learning-based intrusion detection technique inspired by federated learning (FL), specifically designed for SDN-enabled CE networks. The application of the approach involves stringent training and testing protocols. In order to improve performance and handle the very complicated training data, the Pearson correlation coefficient (PCC) approach of feature selection (FS) is used in this work. Based on the UNSW-NB15 attack detection dataset, the results show that this approach performs better than other current security solutions, making it a promising option for next-generation smart CE networks. Comprehensive experimental evaluations demonstrated the value of the suggested intrusion detection system (IDS), which demonstrated exceptional intrusion detection accuracy in every domain under investigation.<\/jats:p>","DOI":"10.1142\/s0218126625504845","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T09:23:48Z","timestamp":1757582628000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimized Deep Learning for Intrusion Detection in Federated IoT Environments"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1343-2219","authenticated-orcid":false,"given":"Divya","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chandigarh University, Mohali, 140413, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8474-9435","authenticated-orcid":false,"given":"Shalli","family":"Rani","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-5882","authenticated-orcid":false,"given":"Ismail","family":"Keshta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Systems, College of Applied Sciences, Almaarefa University, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5106-7609","authenticated-orcid":false,"given":"Mohammad","family":"Shabaz","sequence":"additional","affiliation":[{"name":"Marwadi University Research Center, Department of Computer Engineering, Faculty of Engineering and Technology, Marwadi University, Rajkot 360003, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-3858","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of AI, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2680-8206","authenticated-orcid":false,"given":"Dina Abdulaziz","family":"AlHammadi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. 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Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"S0218126625504845BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2016.10.003"},{"key":"S0218126625504845BIB002","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0268-2"},{"key":"S0218126625504845BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2022.3232478"},{"key":"S0218126625504845BIB004","unstructured":"Statista, Consumer electronics, https:\/\/www.statista.com\/outlook\/dmo\/ecommerce\/electronics\/consumer-electronics\/worldwide, Accessed on 28 July 2022."},{"key":"S0218126625504845BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3023430"},{"key":"S0218126625504845BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2020.3047606"},{"key":"S0218126625504845BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3079916"},{"key":"S0218126625504845BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCME57830.2023.10252518"},{"key":"S0218126625504845BIB009","doi-asserted-by":"crossref","unstructured":"A. Wani and S. Revathi, Analyzing threats of IoT networks using SDN based intrusion detection system (SDIoT-IDS),\n                      Smart and Innovative Trends in Next Generation Computing Technologies: Third Int. Conf., NGCT 2017\n                      , Vol.\n                      3\n                      , Dehradun, India, 30\u201331 October 2017, Revised Selected Papers, Part II (Springer, Singapore, 2018), pp. 536\u2013542.","DOI":"10.1007\/978-981-10-8660-1_41"},{"key":"S0218126625504845BIB010","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11030495"},{"key":"S0218126625504845BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3299606"},{"key":"S0218126625504845BIB012","volume":"70","author":"Babbar H.","year":"2023","journal-title":"IEEE Trans. Consum. 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