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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

Khalid A. Alissa1, Mohammed Maray2, Areej A. Malibari3, Sana Alazwari4, Hamed Alqahtani5, Mohamed K. Nour6, Marwa Obbaya7, Mohamed A. Shamseldin8, Mesfer Al Duhayyim9,*

1 SAUDI ARAMCO Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
2 Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
3 Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
6 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
7 Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
8 Department of Mechanical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt
9 Department of Computer Science, College of Sciences and Humanities- Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email:

Computers, Materials & Continua 2023, 74(3), 5349-5367.


Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.


Cite This Article

K. A. Alissa, M. Maray, A. A. Malibari, S. Alazwari, H. Alqahtani et al., "Optimal deep learning model enabled secure uav classification for industry 4.0," Computers, Materials & Continua, vol. 74, no.3, pp. 5349–5367, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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