Cybersecurity and Artificial Intelligence in Unmanned Aerial Vehicles: Emerging Challenges and Advanced Countermeasures

Abstract

The increasing adoption of artificial intelligence (AI)-driven unmanned aerial vehicles (UAVs) in military, commercial, and surveillance operations has introduced significant security challenges, including cyber threats, adversarial AI attacks, and communication vulnerabilities. This paper presents a comprehensive review of the key security threats and challenges faced by AI-powered UAVs, such as unauthorized access, GPS spoofing, adversarial manipulations, and UAV hijacking. We analyze advanced solutions including blockchain-secured UAV networks, post-quantum cryptography (PQC), adversarial AI training, self-healing AI models, and multi-factor authentication (MFA), which collectively strengthen UAV cybersecurity defenses. Our findings highlight the critical role of emerging technologies, including self-adaptive AI-driven UAVs capable of detecting and learning from novel cyber threats autonomously. We also discuss the integration of 6 G-powered communication networks for secure and ultra-fast encrypted transmissions, as well as Edge AI computing that enables real-time, onboard threat detection without cloud dependency. Furthermore, decentralized intelligence models and blockchain-based authentication are shown to enhance security in UAV swarms by preventing unauthorized infiltration. Overall, this review emphasizes the necessity of multilayered security frameworks that combine AI techniques, cryptographic measures, and decentralized swarm protection to ensure resilient, autonomous, and secure UAV operations in complex and high-risk environments.