IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities

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

Information and communications technologies (ICT) in higher education teaching—a tale of gradualism rather than revolution

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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

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Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data

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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

Smart CEI Moncloa: An IoT-based Platform for People Flow and Environmental Monitoring on a Smart University Campus

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Operational neural networks

Serkan Kiranyaz, B., İnce, T., Iosifidis, A., & Gabbouj, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04780-3 · Cited by 121

lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning

Hazman, C., Guezzaz, A., Benkirane, S., & Azrour, M. (2022) · Cluster Computing · doi:10.1007/s10586-022-03810-0 · Cited by 105

Wearable Fall Detector Using Recurrent Neural Networks

Luna-Perejón, F., Domínguez-Morales, M., & Balcells, A. (2019) · Sensors · doi:10.3390/s19224885 · Cited by 99

A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest – RFTRUST

Prathapchandran, K., & Janani, T. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108413 · Cited by 88

PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Lemenkova, P. (2019) · AQUATIC RESEARCH · doi:10.3153/ar19009 · Cited by 75

An Anomaly Mitigation Framework for IoT Using Fog Computing

Lawal, M. A., Shaikh, R. A., & Hassan, S. R. (2020) · Electronics · doi:10.3390/electronics9101565 · Cited by 73

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Imran, I., Jamil, F., & Kim, D. (2021) · Sustainability · doi:10.3390/su131810057 · Cited by 71

Sequential attention mechanism for weakly supervised video anomaly detection

Ullah, W., Ullah, F. U. M., Khan, Z. A., & Baik, S. W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120599 · Cited by 67

SMS: A Secure Healthcare Model for Smart Cities

Tripathi, G., Ahad, M. A., & Paiva, S. (2020) · Electronics · doi:10.3390/electronics9071135 · Cited by 64

Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments

Preuveneers, D., Tsingenopoulos, I., & Joosen, W. (2020) · Sensors · doi:10.3390/s20041176 · Cited by 60

An IoT Enable Anomaly Detection System for Smart City Surveillance

Islam, M., Dukyil, A. S., Alyahya, S., & Habib, S. (2023) · Sensors · doi:10.3390/s23042358 · Cited by 53

Engineering the application of machine learning in an IDS based on IoT traffic flow

Prazeres, N., Costa, R. L. D. C., Santos, L., & Rabadao, C. M. D. S. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200189 · Cited by 52

Vision transformer attention with multi-reservoir echo state network for anomaly recognition

Ullah, W., Hussain, T., & Baik, S. W. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103289 · Cited by 47

A semi-supervised deep learning based video anomaly detection framework using RGB-D for surveillance of real-world critical environments

Khaire, P., & Kumar, P. (2022) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2022.301346 · Cited by 44

Smart Infrastructure Design for Smart Cities

Ota, K., Kumrai, T., Dong, M., Kishigami, J. J., & Guo, M. (2017) · IT Professional · doi:10.1109/mitp.2017.3680957 · Cited by 44

MUD enabled deep learning framework for anomaly detection in IoT integrated smart building

Mirdula, S., & Roopa, M. (2023) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2023.100186 · Cited by 37

Dense Air Quality Sensor Networks: Validation, Analysis, and Benefits

Zaidan, M. A., Xie, Y., Motlagh, N. H., Wang, B., Nie, W., Nurmi, P., Tarkoma, S., Petäjä, T., Ding, A., & Kulmala, M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3216071 · Cited by 37

Anomaly Detection Approach Based on Deep Neural Network and Dropout

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Industrial network-based behavioral anomaly detection in AI-enabled smart manufacturing

Kim, H., & Shon, T. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04408-4 · Cited by 29

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder

Zhu, J.-J., Zhang, N.-J., Wei, T., & Chen, H. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24086896 · Cited by 26

Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis

Garcia-Font, V., Garrigues, C., & Rifà-Pous, H. (2018) · Sensors · doi:10.3390/s18103198 · Cited by 25

Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

Ajao, L. A., & Apeh, S. T. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200216 · Cited by 25

Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks

Beltrán-Hernández, J. G., Digital Signal Processing and Telematics Groups, E. D. O. T. C. I.-S. (., Ruiz-Pinales, J., López-Rodríguez, P., López-Ramírez, J. L., & Avina‐Cervantes, J. G. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020293 · Cited by 21

Efficient Approach for Anomaly Detection in IoT Using System Calls

Shamim, N., Asim, M., Baker, T., & Awad, A. I. (2023) · Sensors · doi:10.3390/s23020652 · Cited by 19

A survey of anomaly detection approaches in Internet of Things

Behniafar, M., Nowroozi, A., & Shahriari, H. R. (2018) · Isecure. · doi:10.22042/isecure.2018.116976.408 · Cited by 19

A Web of Things based eco-system for urban computing - towards smarter cities

Kamilaris, A., Pitsillides, A., Prenafeta-Boldú, F. X., & Ali, M. I. (2017) · doi:10.1109/ict.2017.7998277 · Cited by 17

An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems

Tomé, E. S., Ribeiro, R. P., Dutra, I., & Rodrigues, A. S. (2023) · Sensors · doi:10.3390/s23104902 · Cited by 16

An IoT-oriented data storage framework in smart city applications

Shwe, H. Y., Jet, T. K., & Chong, P. H. J. (2016) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2016.7763446 · Cited by 16

Operation and maintenance(O&M) for data center: An intelligent anomaly detection approach

Xiao, X.-S., Sun, J. D., & Yang, J. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.06.030 · Cited by 11

Time domain graph-based anomaly detection approach applied to a real industrial problem

Júnior, W. J. D. A., Campos, F. V., Costa, A. C. A. A., Salis, T. T., Magalhães, E., Torres, L. C. B., & Braga, A. P. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103714 · Cited by 10

Time dependent anomaly detection system for smart environment using probabilistic timed automaton

Venkatraman, S., Muthusamy, P., Balusa, B. C., Jayasankar, T., Kavithaa, G., Sekar, K. R., & Bharatiraja, C. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02769-3 · Cited by 8

Group anomaly detection for spatio-temporal collective behaviour scenarios in smart cities

Lohrer, A., Binder, J. J., & Kröger, P. (2022) · ACM SIGSPATIAL International Workshop on Computational Transportation Science (IWCTS) · doi:10.1145/3557991.3567801 · Cited by 7

Anomaly Detection Techniques in Smart City: A Review from a Framework Perspective

Prabowo, O. M., Supangkat, S. H., & Mulyana, E. (2021) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss53185.2021.9533252 · Cited by 6

Unfolding the network dataset to understand the contribution of features for detecting malicious activities using AI/ML

Khatkar, M., Kumar, K., & Kumar, B. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.04.391 · Cited by 5

Messages Evaluation for BDSBAS Single-Frequency Service

Wang, H., Fang, K., Dan, Z., Li, X., Guo, K., Wang, Z., & Zhu, Y. (2022) · Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation · doi:10.33012/2022.18237 · Cited by 3

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

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

Towards a conceptual framework for AI-driven anomaly detection in smart city IoT networks for enhanced cybersecurity

Zeng, H., Yunis, M. M., Khalil, A., & Mirza, N. (2024) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2024.100601 · Cited by 56

Generative AI of things for sustainable smart cities: Synergizing cognitive augmentation, resource efficiency, network traffic, cybersecurity, and anomaly detection for environmental performance

Bibri, S. E., & Huang, J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106826 · Cited by 50

A Survey on anomaly detection in IoT: Techniques, challenges, and opportunities with the integration of 6G

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

A Survey Paper based: Network Anomaly Detection for IoT Systems in Smart City using Machine Learning Approach

Dagli, M. K., Padheriya, H., Devani, M. P., & Patel, J. (2025) · African Journal of Biomedical Research · doi:10.53555/ajbr.v28i2s.7290 · Cited by 2

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

Hasani, Z., Krrabaj, S., & Krasniqi, M. (2024) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v18i03.44467 · Cited by 5

Scalable and Privacy-Aware IoT Anomaly Detection Using Machine Learning in Smart City Environments

Zakwani, M. A., Zakwani, S. A., Abrar, M., Husaini, Y. A., Husaini, M. A. S. A., & Kishri, W. A. (2025) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca66035.2025.11431062

Enhancing Smart Cities Through Real-Time Insights and Safety: A Comparative Study of Supervised Machine Learning Algorithms for Anomaly Detection in Emerging Urban Landscapes

Noor, U., Kanwal, R., & Rashid, Z. (2024) · Technical Journal

SmartCityGuard: AI-Driven Anomaly Detection for IoT-Enabled Smart City Infrastructure

Goh, A. L. H., Ho, G. R. J., Low, S. Q., Tan, G. J. A., Tan, S. Y. X., Chung, W. Y. S., & Guo, H. (2026) · Singapore Institute of Technology · doi:10.25447/sit.33536341