aspg-2-4035-1791416973 20261007234933 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2026 17 2 Adil. O. Y. Mohamed 1Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia Yousef Asiri Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia Manahill I. A. Anja Computer Sciences Program, Department of Mathematics, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Bandar M. Alghamdi Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia Abdelgalal O. I. Abaker Department of Administrative Sciences, Applied College in Khamis Mushait, King Khalid University, Abha, Saudi Arabia Mnahil M. Bashier Department of Mathematics, Faculty of Science, Northern Border University, Arar, Saudi Arabia 16.00 Normal false false false TR X-NONE 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" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" Name="toc 3"/> UnhideWhenUsed="true" Name="toc 4"/> 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Web 1"/> Name="Table Web 2"/> Name="Table Web 3"/> Name="Balloon Text"/> Name="Table Theme"/> Name="List Paragraph"/> Name="Intense Quote"/> Name="Subtle Emphasis"/> Name="Intense Emphasis"/> Name="Subtle Reference"/> Name="Intense Reference"/> UnhideWhenUsed="true" Name="Bibliography"/> UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> Name="Grid Table 1 Light Accent 1"/> Name="Grid Table 6 Colorful Accent 1"/> Name="Grid Table 7 Colorful Accent 1"/> Name="Grid Table 1 Light Accent 2"/> Name="Grid Table 6 Colorful Accent 2"/> Name="Grid Table 7 Colorful Accent 2"/> Name="Grid Table 1 Light Accent 3"/> Name="Grid Table 6 Colorful Accent 3"/> Name="Grid Table 7 Colorful Accent 3"/> Name="Grid Table 1 Light Accent 4"/> Name="Grid Table 6 Colorful Accent 4"/> Name="Grid Table 7 Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} 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 48 63 4035 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.170205 https://www.americaspg.com/journal/2/article/4035 https://www.americaspg.com/storage/01766696686.pdf