An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection

Author:

Alturki Nazik1ORCID,Aljrees Turki2ORCID,Umer Muhammad3ORCID,Ishaq Abid3ORCID,Alsubai Shtwai4ORCID,Saidani Oumaima1ORCID,Djuraev Sirojiddin5,Ashraf Imran6ORCID

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

2. College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia

3. Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

4. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia

5. Department of Software Engineering, New Uzbekistan University, Tashkent 100007, Uzbekistan

6. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

Abstract

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber–physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.

Funder

University of Hafr Al Batin

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference80 articles.

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