With the widespread application of Internet of Things (IoT) technology in smart cities, its security issues have become increasingly prominent. Fuzzing, as an efficient automated vulnerability discovery technique, has been widely used in IoT security assessment. However, current research mostly focuses on general IoT environments or specific device types, lacking a systematic analysis of the complex, dynamic, and deeply integrated context of smart cities. This paper presents a review and integration of 42 representative IoT fuzzing studies published between 2021 and 2025, analyzed via an eight-dimensional analytical framework. It reveals significant gaps with reports on real-world attacks on the IoT systems between current research and the practical security needs of smart cities across three dimensions: device, protocol, and methodology. Based on this, this paper innovatively proposes: (1) an Observability-Complexity Based IoT Device Classification Model based on device observability and business logic complexity, providing a navigation chart for migrating testing capabilities across devices; (2) a technology migration framework based on protocol feature matching, facilitating rapid coverage of emerging and vertical protocols; (3) a methodological evolution path from “vulnerability mining” to “system resilience probing.” This research aims to promote the future role of IoT fuzzing in the assessment and assurance of smart city security resilience by providing structured analytical tools and clear research directions.
Li et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: