Artificial Intelligence Methods for Unmanned Aerial Vehicles Cybersecurity: A Comprehensive Survey

Unmanned Aerial Vehicles (UAVs) have been widely used in recent years in various applications thanks to advances in communication, Internet of Things and electronics. However, despite the advantages they offer, reports of cybersecurity attacks represent a serious threat to their operation. Classic cryptographic-based solutions and traditional intrusion detection approaches generally struggle to deal with these attacks due to their adaptive and stealthy nature. In this context, Artificial Intelligence (AI) models emerged as potential solutions that hold great promise in addressing this type of attacks. However, most related surveys presented fragmented picture of the state-of-the-art failing to cover all sub-types of AI models, and sometimes not following structured taxonomies or describing popular datasets that were used in the literature. In this paper, we bridge this gap by proposing a novel and comprehensive survey that classifies UAV security research according to the type of AI model, the cyber attacks it thwarts and the related security properties it enforces. This taxonomy does not stop at describing Machine Learning (ML) and Deep Learning (DL) approaches, but it also dives into emerging approaches such as Federated Learning (FL), Reinforcement Learning (RL), Graph Neural Network (GNN) and Generative AI (GAI). We also classify the threat vector according to the layer in the UAV functional stack where the attack takes place. In addition, we describe the datasets, tools and evaluation metrics that were mostly used in the literature. We conclude the survey by summarizing the key insights, discussing the open challenges and enumerating future research directions. We aim that this survey serves as a reference for cyber security researchers and practitioners who tackle UAV security using AI.