{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:06:56Z","timestamp":1762348016370,"version":"build-2065373602"},"reference-count":0,"publisher":"SASA Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JISIS"],"published-print":{"date-parts":[[2025,8,30]]},"abstract":"<jats:p>Devices associated with the Internet of Things (IoT) are rapidly becoming ubiquitous, while IoT \nservices are expanding their reach to become increasingly pervasive. IoT devices and applications \nare increasingly targets of attacks as a result of their rising popularity. IoT has long been a target of \ncyberattacks; however, as it becomes more pervasive in everyday lives and communities, people \nmust take this threat with greater attention. Researchers have been trying to have a complete \nunderstanding of the threats and assaults that can harm IoT networks since safeguarding the IoT is \na major issue. This work discovers the use of deep learning (DL) models in several facets of \ncybersecurity, analysing the results and finding parallels to machine learning (ML). An ensemble of \nLong Short-Term Memory (LSTM), Deep Convolutional Neural Network (DCNN), and Elman \nNeural Network (ENN) classifiers is used by the Multi-Class Classifier with Adaptive Horse \nOptimization (MCC-AHO) to identify the assault types. In this work, an ensemble technique known \nas majority voting was utilized, which involved optimizing the weights of the many classifiers to \nachieve the best possible result. This proposed model for cyber-attack detection assesses their \neffectiveness using the most recent CIC IoT Dataset 2023, achieving an accuracy of 98.16 %.<\/jats:p>","DOI":"10.58346\/jisis.2025.i3.037","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:23:58Z","timestamp":1762341838000},"page":"539-559","source":"Crossref","is-referenced-by-count":0,"title":["Cyber Security Identification Model Over an Ensemble Deep  Learning System in IoT Networks"],"prefix":"10.58346","volume":"15","author":[{"given":"Boyella Mala","family":"Konda Reddy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"37075","published-online":{"date-parts":[[2025,8,30]]},"container-title":["Journal of Internet Services and Information Security"],"original-title":[],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:02:57Z","timestamp":1762347777000},"score":1,"resource":{"primary":{"URL":"https:\/\/jisis.org\/wp-content\/uploads\/2025\/11\/2025.I3.037.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,30]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,8,30]]},"published-print":{"date-parts":[[2025,8,30]]}},"URL":"https:\/\/doi.org\/10.58346\/jisis.2025.i3.037","relation":{},"ISSN":["2182-2069","2182-2077"],"issn-type":[{"type":"print","value":"2182-2069"},{"type":"electronic","value":"2182-2077"}],"subject":[],"published":{"date-parts":[[2025,8,30]]}}}