The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
Open accessAbstract
This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted.
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“detectors that achieve >99% accuracy on a single dataset routinely lose 10-20 percentage points when transferred cross-dataset”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [51]
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Metadata
- Authors
- Villegas-Ch, W., Govea, J., & Jaramillo-Alcazar, A.
- Affiliations
- Universidad de Las Américas (EC)
- Escuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador
- Corresponding author
- Villegas-Ch, William
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-10-05
- Volume
- 13
- Issue
- 19
- Pages
- 10977
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4387376803
- Cited by
- 48
- References
- 44
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 13
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- anomaly detection, Internet of Things, smart cities, critical infrastructure cybersecurity, Isolation Forest, recurrent neural networks, variational autoencoder, early anomaly detection
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