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

TL;DR

This paper surveys AI-based intrusion detection techniques for UAVs in smart cities, highlighting security challenges, datasets, and future research directions to enhance urban UAV security systems.

Contribution

It provides a comprehensive synthesis of UAV security challenges, reviews AI-driven intrusion detection methods, and consolidates datasets, offering a roadmap for future research in UAV cybersecurity within smart urban ecosystems.

Findings

AI techniques can effectively detect cyber and physical threats to UAVs.

Public datasets are available for developing and evaluating IDS for UAVs.

Identifies key research directions like scalability, robustness, and privacy in UAV security.

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.