As smart cities advance, Internet of Things (IoT) devices present cybersecurity challenges that call for innovative solutions. This paper presents a conceptual model for using AI-enabled anomaly detection systems to identify anomalies and security threats in smart city IoT networks. The foundation is supported by the Complex Adaptive Systems (CAS) theory, Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB). In this framework, the importance of user engagement in ensuring effective AI-driven cybersecurity solutions is underlined with an emphasis on technological readiness and human interaction with AI. By fostering a security-conscious culture through continuous education and skills development, this research provides actionable insights for enhancing the resilience of smart cities against evolving cyber threats. The proposed framework lays the groundwork for future empirical studies and offers practical guidance for policymakers and urban planners dedicated to safeguarding the digital infrastructures of potentially tomorrow's cities – the smart cities.

中文翻译：

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迈向智慧城市物联网网络中 AI 驱动异常检测的概念框架，以增强网络安全


随着智慧城市的发展，物联网 （IoT） 设备带来了网络安全挑战，需要创新的解决方案。本文提出了一个概念模型，用于使用支持 AI 的异常检测系统来识别智慧城市 IoT 网络中的异常和安全威胁。该基础由复杂自适应系统 （CAS） 理论、技术验收模型 （TAM） 和计划行为理论 （TPB） 提供支持。在这个框架中，强调了用户参与在确保有效的 AI 驱动的网络安全解决方案方面的重要性，并强调了技术准备和人类与 AI 的交互。通过持续教育和技能发展培养安全意识文化，本研究为增强智慧城市抵御不断变化的网络威胁的弹性提供了可行的见解。拟议的框架为未来的实证研究奠定了基础，并为致力于保护未来城市（智慧城市）的数字基础设施的政策制定者和城市规划者提供了实用指导。