aspg-18-4148-1791423564 20261008013924 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 2 M. Sindhuja Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District - 603 203, Tam Noorfazila Kamal Department of Electrical, Electronics & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia Kalaivani Chellappan Integrated Systems Engineering and Advanced Technologies (INTEGRA), Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia 16.00 Normal false false false EN-US ZH-CN AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" 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Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri",sans-serif; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:ZH-CN;} The rapid expansion of Internet of Things (IoT) devices has significantly amplified cybersecurity risks, thereby necessitating advanced anomaly detection mechanisms. This research introduces a hybrid detection framework tailored for IoT networks, combining deep learning architectures with bio-inspired optimization techniques. At the core of the framework lies the IoT Autoencoder-Based Feature Extraction Network (IoTAE-FEN), designed to minimize data dimensionality while preserving key discriminative features. To further refine the selected attributes, a Binary Multi-Objective Enhanced Gray Wolf Optimization (BMOEGWO) strategy, modeled on the cooperative hunting behavior of gray wolves, is employed. For the classification phase, Random Forest (RF) is integrated, resulting in the proposed AE-BMOEGWO-RF hybrid model. The effectiveness of this approach was validated on benchmark datasets, including NSL-KDD and TON-IoT. Experimental findings highlight a feature selection accuracy of 96.85% on the TON-IoT dataset and an overall classification performance of 97.81% on NSL-KDD. Comparative evaluations against existing techniques underscore the framework’s superior detection capability, emphasizing its potential to strengthen IoT network security by addressing longstanding challenges in feature extraction and selection for anomaly detection. 2025 369 391 4148 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170224 https://www.americaspg.com/journal/18/article/4148 https://www.americaspg.com/storage/61759948418.pdf