Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4035 2019 2019 1Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia Adil. Adil. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia Yousef Asiri Computer Sciences Program, Department of Mathematics, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Manahill I. A. Anja Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia Bandar M. Alghamdi Department of Administrative Sciences, Applied College in Khamis Mushait, King Khalid University, Abha, Saudi Arabia Abdelgalal O. I. Abaker Department of Mathematics, Faculty of Science, Northern Border University, Arar, Saudi Arabia Mnahil M. Bashier The Industrial Internet of Things (IIoT) is the incorporation of industrial processes with smart technology and interconnected devices to improve productivity and efficiency. The need for robust cybersecurity measures is crucial as the IIoT environment becomes vital to critical infrastructure in industries. Cybersecurity in IIoT is paramount to secure against possible threats, which ensures the integrity and resilience of industrial operations. Intrusion detection systems (IDSs) are instrumental in detecting anomalies, unauthorized access, or malicious activities. The incorporation of deep learning (DL) further reinforces the cybersecurity posture of the IIoT network. DL approach excels in analyzing complex and large datasets, which enables the detection of complex cyber threats by learning anomalies and patterns. Industrial processes can operate with heightened security, securing sensitive information, and critical infrastructure, and maintaining the reliability of a connected system in the industrial landscape by combining IIoT cybersecurity with innovative intrusion detection and DL technologies. Therefore, this article proposes an Integration of Coot Optimization Algorithm-based Feature Subset Search with Deep Learning for Cybersecurity (COAFSS-DLCS) technique on IIoT network. The objective is in the effectual recognition and classification of cyberattacks in the IIoT environment. Initially, the COAFSS-DLCS method uses min-max scalar to transform the input dataset into a suitable format. Furthermore, the COAFSS-DLCS employs the COAFSS approach for choosing an optimal feature subset. Additionally, the stacked long short-term memory autoencoder (SLSTM-AE) model is employed for classification. Moreover, the parameters of the SLSTM-AE classifier are fine-tuned using the Arithmetic Optimization Algorithm (AOA) for improved performance. A comprehensive empirical validation of the COAFSS-DLCS approach is performed under the UNSW_NB15 and UCI_SECOM datasets. The simulation outputs inferred the power of the COAFSS-DLCS over other methods. 2026 2026 48 63 10.54216/JCIM.170205 https://www.americaspg.com/articleinfo/2/show/4035