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

Area of Science:

• Cyber-Physical Systems
• Aerospace Engineering
• Computer Science

Background:

• Small drone technology, leveraging the Internet of Things (IoT), faces significant security and privacy vulnerabilities due to architectural limitations.
• Ensuring secure and reliable drone networks is crucial for optimal performance and to prevent safety, security, and privacy breaches in civil and defense applications.
• Current drone architectures require enhancements in data transformation and privacy mechanisms to meet evolving domain requirements.

Purpose of the Study:

• To investigate current safety, security, and privacy trends in drone technology and the Internet of Drones (IoD).
• To propose an intelligent framework that enhances the security and resilience of IoT-aided drones against cyber-attacks.
• To develop and validate a novel approach for secure drone network design.

Main Methods:

• Development of a novel framework incorporating intelligent machine learning models into IoT-aided drone architecture.
• Construction of a new, merged dataset combining a dedicated drone dataset with two benchmark datasets.
• Evaluation of the proposed framework's performance against existing algorithms.

Main Results:

• The proposed framework achieves high accuracy, reaching 99.89% on the drone dataset and 91.64% on the merged dataset.
• Demonstrated superior performance compared to previous algorithms in securing drone networks.
• The framework effectively mitigates cyber-security threats, enhancing drone system resilience.

Conclusions:

• The intelligent framework offers a robust approach to improving the security and resilience of IoT-aided aerial vehicle systems.
• Addresses critical security challenges in interconnected drone systems and cyber-physical satellite systems.
• Highlights the importance of secure drone networks impervious to interception and intrusion.