{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T12:21:33Z","timestamp":1774182093289,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging. As the IIoT becomes integral to critical infrastructure, ensuring security is crucial to prevent disruptions and data breaches. Traditional IDS approaches often fall short against evolving threats, highlighting the need for intelligent and adaptive solutions. While deep learning (DL) offers strong capabilities for pattern recognition, single-model architectures often lack robustness. Thus, hybrid and optimized DL models are increasingly necessary to improve detection performance and address data imbalance and noise. In this study, we propose an optimized hybrid DL framework that combines a transformer, generative adversarial network (GAN), and autoencoder (AE) components, referred to as Transformer\u2013GAN\u2013AE, for robust intrusion detection in Edge and IIoT environments. To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChOA) for hyperparameter tuning and feature refinement. The proposed method is evaluated using three recent and comprehensive benchmark datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, widely recognized as standard testbeds for IIoT intrusion detection research. Extensive experiments are conducted to assess the model\u2019s performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). Evaluation metrics include accuracy, recall, AUC, and run time. Results demonstrate that the proposed Transformer\u2013GAN\u2013AE framework outperforms all baseline methods, achieving a best accuracy of 98.92%, along with superior recall and AUC values. The integration of IChOA enhances GAN stability and accelerates training by optimizing hyperparameters. Together with the transformer for temporal feature extraction and the AE for denoising, the hybrid architecture effectively addresses complex, imbalanced intrusion data. The proposed optimized Transformer\u2013GAN\u2013AE model demonstrates high accuracy and robustness, offering a scalable solution for real-world Edge and IIoT intrusion detection.<\/jats:p>","DOI":"10.3390\/fi17070279","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T09:24:18Z","timestamp":1750757058000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Optimized Transformer\u2013GAN\u2013AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets"],"prefix":"10.3390","volume":"17","author":[{"given":"Ahmad","family":"Salehiyan","sequence":"first","affiliation":[{"name":"School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9528-6131","authenticated-orcid":false,"given":"Pardis Sadatian","family":"Moghaddam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0752-8054","authenticated-orcid":false,"given":"Masoud","family":"Kaveh","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Aalto University, 02150 Espoo, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.dcan.2024.02.007","article-title":"Integration of Data Science with the Intelligent IoT (IIoT): Current Challenges and Future Perspectives","volume":"11","author":"Ullah","year":"2025","journal-title":"Digit. 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