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

Author(s):

Nazik Alturki Turki Aljrees Muhammad Umer Abid Ishaq Shtwai Alsubai Oumaima Saidani Sirojiddin Djuraev Imran Ashraf

Journal

MDPI AG

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. © 2023 by the authors.

Concluding remarks

The current study focused on proposing an IoT drone-based cyber-security framework network. This framework employs a voting ensemble of ML algorithms and employs data from various sources, such as network information, drones, and sensors, to identify securitylevel patterns and detect security attacks. The proposed architecture combines several cutting-edge technologies, such as machine learning, artificial intelligence, data fusion, and anomaly detection, to build a powerful and adaptable security solution. The framework can identify both known and unknown threats by utilizing the strength of advanced algorithms, allowing for quick response and mitigation actions. The proposed framework was tried on the drone dataset and was able to demonstrate robust results for cyber-attack identification in real time, achieving an accuracy rate of 99.89%, which surpasses previous approaches. The performance of the proposed framework was evaluated on a newly constructed merged dataset in terms of accuracy, recall, precision, and F1-score. The RegressionNet model is proposed to accurately identify attack types accurately and shows its authority and strength. This framework can be deployed to detect vulnerabilities in other domains as well in the future. Furthermore, in future work, we will also focus on adding a malware attack prevention layer in the proposed framework. Author Contributions: Conceptualization, N.A. and T.A.; Data curation, T.A. and M.U.; Formal analysis, N.A., A.I. and O.S.; Funding acquisition, N.A.; Investigation, M.U. and S.D.; Methodology, S.A. and S.D.; Project administration, T.A. and S.A.; Software, A.I., S.A. and O.S.; Supervision, I.A.; Validation, I.A.; Visualization, O.S. and S.D.; Writing—original draft, M.U., N.A., O.S. and A.I.; Writing—review and editing, I.A. All authors have read and agreed to the published version of the manuscript. Funding:Wewould like to thank the University ofHafr Al Batin for their invaluable support and Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R333), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Reference details

DOI

10.3390/s23167154

Resource type

Journal Article

Year of Publication

2023

ISSN Number

1424-8220

Publication Area

Dual-use cybersecurity

Date Published

2023-08-14

URL

https://www.mdpi.com/1424-8220…

How to cite this reference:

Alturki, N., Aljrees, T., Umer, M., Ishaq, A., Alsubai, S., Saidani, O., … Ashraf, I. (2023). An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection. MDPI AG. https://doi.org/10.3390/s23167154 (Original work published)