AI-driven Intrusion Detection for UAV in Smart Urban Ecosystems: A Comprehensive Survey

TL;DR

This survey addresses AI-driven intrusion detection for UAVs operating in smart cities by examining dual intrusion threats: cyber-attacks on UAV communication and unauthorized physical UAVs. It proposes a unified IDS taxonomy that integrates cyber and physical detection across onboard, edge, and ground-control tiers, supported by AI techniques spanning supervised, unsupervised, and reinforcement learning, as well as multimodal data fusion. The authors consolidate publicly available UAV datasets for network/vision modalities and outline ten open research directions, including scalability, robustness, explainability, data scarcity, automation, and privacy-preserving learning, while discussing practical deployment challenges such as resource constraints and regulatory issues. Overall, the work highlights the need for a holistic, interoperable, and data-driven defense layer that can scale to city-wide UAV fleets and coexist with other smart-city systems, while calling for standardized benchmarks and UAV-native datasets to enable fair comparison and reproducibility.

Abstract

UAVs have the potential to revolutionize urban management and provide valuable services to citizens. They can be deployed across diverse applications, including traffic monitoring, disaster response, environmental monitoring, and numerous other domains. However, this integration introduces novel security challenges that must be addressed to ensure safe and trustworthy urban operations. This paper provides a structured, evidence-based synthesis of UAV applications in smart cities and their associated security challenges as reported in the literature over the last decade, with particular emphasis on developments from 2019 to 2025. We categorize these challenges into two primary classes: 1) cyber-attacks targeting the communication infrastructure of UAVs and 2) unwanted or unauthorized physical intrusions by UAVs themselves. We examine the potential of Artificial Intelligence (AI) techniques in developing intrusion detection mechanisms to mitigate these security threats. We analyze how AI-based methods, such as machine/deep learning for anomaly detection and computer vision for object recognition, can play a pivotal role in enhancing UAV security through unified detection systems that address both cyber and physical threats. Furthermore, we consolidate publicly available UAV datasets across network traffic and vision modalities suitable for Intrusion Detection Systems (IDS) development and evaluation. The paper concludes by identifying ten key research directions, including scalability, robustness, explainability, data scarcity, automation, hybrid detection, large language models, multimodal approaches, federated learning, and privacy preservation. Finally, we discuss the practical challenges of implementing UAV IDS solutions in real-world smart city environments.

Figures (13)

• Figure 1: A detailed structure of the survey content.
• Figure 2: The basic form of a UAV Architecture is composed of a fleet of UAVs, potentially equipped with a wide array of sensors and connected via wireless communication links. The UAVs can communicate with external entities such as ground cellular networks, satellites, and HAPs. A ground control station controls them to ensure the management and coordination of the fleet, as well as the processing of the data collected.
• Figure 3: Growth of the European drone market size from 2016 up to 2027 in Million USD based on different applications - Graphical Research EuropenDroneMarket.
• Figure 4: Growth of the global market size of drones per year from 2023 up to 2034 precedenceresearch.
• Figure 5: Map of entry points and adversarial actions across airborne, links and RF, ground and maintenance, and human layers, indicating typical IDS tiers for onboard, edge fusion, and ground control correlation.