<b>Cybersecurity-Enhanced GPS Spoofing Protection and Secure Information Transmission for UAVs</b>

Unmanned Aerial Vehicles (UAVs) are increasingly vulnerable to cybersecurity threats such as GPS spoofing and communication breaches, necessitating advanced protection mechanisms. This paper presents a comprehensive framework integrating cybersecurity principles with AI and machine learning (ML) algorithms to enhance GPS spoofing protection and secure information transmission for UAVs. The framework employs cryptographic signal watermarking and AI-driven signal pattern analysis for real-time spoofing detection, supported by multi-factor signal validation through INS, geomagnetic sensors, and Visual SLAM. Secure communication is ensured via dynamic encryption, end-to-end data masking using homomorphic encryption, and neural network intrusion detection systems (IDS). Cyber-resilient pathways leverage behavior-based access control, distributed secure nodes, and AI-driven dynamic routing. Blockchain technology ensures GPS data integrity, while reinforcement learning (RL) models enable adaptive threat mitigation and self-learning security protocols. Incident response includes detailed cybersecurity logging, emergency navigation using onboard sensors, and encrypted mission data recall. This integrated approach provides robust, adaptive, and scalable solutions to address evolving cybersecurity challenges in UAV operations.