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

2026-01-27

AI Summary

This study addresses the dual security threats of cyberattacks and physical intrusions faced by unmanned aerial vehicles (UAVs) in smart cities. Surveying research from 2019 to 2025, this work proposes the first AI-driven intrusion detection framework that integrates network traffic and visual modalities, leveraging machine learning, deep learning, and computer vision for cross-domain anomaly detection and target identification. Key contributions include the establishment of a unified multimodal threat taxonomy, the consolidation of publicly available datasets to support intrusion detection system (IDS) development, and the articulation of ten forward-looking research directions—including multimodal fusion, large language models, and federated learning—to provide an actionable roadmap for real-world deployment.

Technology Category

• Computer Vision: Multi-modal Vision
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• UAV security
• intrusion detection
• cyber-attacks
• physical intrusion
• smart cities

Innovation

• AI-driven intrusion detection
• unified cyber-physical security
• multimodal UAV datasets
• federated learning for UAVs
• large language models in IDS