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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
SmartSantander: IoT experimentation over a smart city testbed
Sánchez, L., Muñoz, L., Galache, J. A., Sotres, P., Santana, J. R., Gutiérrez, V., Ramdhany, R., Gluhak, A., Krčo, S., Theodoridis, E., et al. (2014) · Computer Networks · doi:10.1016/j.bjp.2013.12.020 · Cited by 704
DL‐IDS: a deep learning–based intrusion detection framework for securing IoT
Otoum, Y., Liu, D., & Nayak, A. R. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3803 · Cited by 354
Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
Alsoufi, M. A., Razak, S. B. A., Siraj, M. M., Nafea, I. T., Ghaleb, F. A. A., Saeed, F., & Nasser, M. (2021) · Applied Sciences · doi:10.3390/app11188383 · Cited by 259
Kirkup, G., & Kirkwood, A. (2005) · Learning Media and Technology · doi:10.1080/17439880500093810 · Cited by 233
An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos
Ullah, W., Ullah, A., Hussain, T., Khan, Z. A., & Baik, S. W. (2021) · Sensors · doi:10.3390/s21082811 · Cited by 193
Variational transformer-based anomaly detection approach for multivariate time series
Wang, X., Pi, D., Zhang, X., Liu, H., & Guo, C. (2022) · Measurement · doi:10.1016/j.measurement.2022.110791 · Cited by 171
Ullah, W., Ullah, A., Hussain, T., Muhammad, K., Heidari, A. A., Ser, J. D., Baik, S. W., & de Albuquerque, V. H. C. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.033 · Cited by 168
Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection
Alhakami, W., Alharbi, A., Bourouis, S., Alroobaea, R., & Bouguila, N. (2019) · IEEE Access · doi:10.1109/access.2019.2912115 · Cited by 131
Álvarez-Campana, M., López, G., Vázquez, E., Villagrá, V. A., & Berrocal, J. (2017) · Sensors · doi:10.3390/s17122856 · Cited by 125
Serkan Kiranyaz, B., İnce, T., Iosifidis, A., & Gabbouj, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04780-3 · Cited by 121
Research works printed but not linked: 4
- Shukla, S., Thakur, S., and Breslin, J.G. (2022). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.
- Ragab, 2022, Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City, Comput. Mater. Contin., 73, 381
- Carpio, 2016, Training OFF-Line Hyperheuristics For Course Timetabling Using K-Folds Cross Validation, La Rev. Program. Matemática Y Softw., 8, 1
- Obama, 2014, Improving Critical Infrastructure Cybersecurity, Cybersecur. Exec. Order 13636 Crit. Infrastruct. Framew., 2009, 27
Cited by
Sorted by citations
Xu, H., Omitaomu, O. A., Sabri, S., Sisi Zlatanova, B., Li, X., & Song, Y. (2024) · Urban Informatics · doi:10.1007/s44212-024-00060-w · Cited by 253
Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers
Sana, L., Nazir, M., Yang, J., Hussain, L., Chen, Y., Ku, C. S., Alatiyyah, M. H., Alateyah, S. A., & Por, L. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3404778 · Cited by 109
Zeng, H., Yunis, M. M., Khalil, A., & Mirza, N. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100601 · Cited by 56
Bibri, S. E., & Huang, J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106826 · Cited by 50
Haider, Z. A., Zeb, A., Rahman, T., Singh, S. K., Akram, R., Arishi, A., & Ullah, I. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111484 · Cited by 40
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
