{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T02:53:50Z","timestamp":1778813630113,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>The rapid expansion of the Internet of Things (IoT) has introduced new vulnerabilities that traditional security mechanisms often fail to address effectively. Signature-based intrusion detection systems cannot adapt to zero-day attacks, while rule-based solutions lack scalability for the diverse and high-volume traffic in IoT environments. To strengthen the security framework for IoT, this paper proposes a deep learning-based anomaly detection approach that integrates Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs). The model is further optimized using the Moth\u2013Flame Optimization (MFO) algorithm for automated hyperparameter tuning. To mitigate class imbalance in benchmark datasets, we employ Generative Adversarial Networks (GANs) for synthetic sample generation alongside Z-score normalization. The proposed CNN\u2013BiGRU + MFO framework is evaluated on two widely used datasets, UNSW-NB15 and UCI SECOM. Experimental results demonstrate superior performance compared to several baseline deep learning models, achieving improvements across accuracy, precision, recall, F1-score, and ROC\u2013AUC. These findings highlight the potential of combining hybrid deep learning architectures with evolutionary optimization for effective and generalizable intrusion detection in IoT systems.<\/jats:p>","DOI":"10.3390\/jcp5040090","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T03:44:39Z","timestamp":1761795879000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["AI-Powered Security for IoT Ecosystems: A Hybrid Deep Learning Approach to Anomaly Detection"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2137-0864","authenticated-orcid":false,"given":"Deepak","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5606-0019","authenticated-orcid":false,"given":"Priyanka Pramod","family":"Pawar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3286-8224","authenticated-orcid":false,"given":"Santosh Reddy","family":"Addula","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3585-5060","authenticated-orcid":false,"given":"Mohan Kumar","family":"Meesala","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1603-7166","authenticated-orcid":false,"given":"Oludotun","family":"Oni","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"given":"Qasim Naveed","family":"Cheema","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"given":"Anwar Ul","family":"Haq","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0327-2450","authenticated-orcid":false,"given":"Guna Sekhar","family":"Sajja","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","article-title":"Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges","volume":"91","author":"Li","year":"2023","journal-title":"Inf. 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