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. Despite the advantages they offer, there have been reports of cybersecurity attacks, which represent serious threats to their operations. Classic cryptographic-based solutions and traditional intrusion detection approaches generally struggle to deal with these attacks due to their adaptive and evolving nature. In this context, artificial intelligence (AI) models emerged as potential solutions that hold great promise in addressing these types of attacks. However, most related surveys presented a fragmented picture of the state of the art, failing to cover all sub-types of AI models, and often did not follow structured taxonomies for describing the literature. In this paper, we bridge this gap by proposing a novel and comprehensive survey inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, defining the search strategy, inclusion and exclusion criteria, selection process, and classification. We also present a cross-dimensional taxonomy 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 also examines 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. Our survey analyzes the common uses of each AI model type in UAV security and discusses its strengths, limitations, and deployment readiness. The outcome of our taxonomy is a quantitative and qualitative analysis providing quantifiable metrics on the covered security properties per model type. We conclude the paper by discussing the key open challenges and future directions in the field. We intend for this survey to serve as a reference for cybersecurity researchers and practitioners who tackle UAV security using AI.