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.

翻译：无人机有潜力彻底改变城市管理，并为市民提供有价值的服务。它们可部署于多种应用场景，包括交通监控、灾害响应、环境监测以及众多其他领域。然而，这种融合引入了新的安全挑战，必须加以解决以确保城市运营的安全可靠。本文基于过去十年（尤其侧重2019年至2025年的进展）的文献报道，对智慧城市中无人机的应用及其相关安全挑战进行了结构化、循证的综合分析。我们将这些挑战归纳为两大类：1）针对无人机通信基础设施的网络攻击；2）无人机自身造成的不必要或未经授权的物理入侵。我们探讨了人工智能技术在开发入侵检测机制以缓解这些安全威胁方面的潜力。我们分析了基于人工智能的方法（如用于异常检测的机器学习/深度学习以及用于目标识别的计算机视觉）如何通过统一应对网络和物理威胁的检测系统，在增强无人机安全方面发挥关键作用。此外，我们整合了适用于入侵检测系统开发和评估的、涵盖网络流量和视觉模态的公开可用无人机数据集。本文最后提出了十个关键研究方向，包括可扩展性、鲁棒性、可解释性、数据稀缺性、自动化、混合检测、大语言模型、多模态方法、联邦学习和隐私保护。最后，我们讨论了在现实智慧城市环境中实施无人机入侵检测系统解决方案所面临的实际挑战。