The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index.
IoT anomaly detection methods and applications: A survey
Open accessAbstract
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.
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Cites
2 retracted
Niraja, K. S., & Rao, S. S. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.01.751 · Cited by 14
RetractedWITHDRAWN: Geographic anomaly detection using IOT principles
Koduru, A., Brihadh, M., Jayanth, R. K., Chandana, N. S., kiran, K. T., Kiran, G. V., & Reddy, M. P. T. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.10.468 · Cited by 1
RetractedChandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018) · IEEE Pervasive Computing · doi:10.1109/mprv.2018.03367731 · Cited by 1,668
Learning under Concept Drift: A Review
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102
Musa, T. H. A., & Bouras, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2102-4_36 · Cited by 1,040
Anomaly Detection for IoT Time-Series Data: A Survey
Cook, A. A., Mısırlı, G., & Fan, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2958185 · Cited by 834
Research works printed but not linked: 7
- M. Dunne, G. Gracioli, S. Fischmeister, A comparison of data streaming frameworks for anomaly detection in embedded systems, in: Proceedings of the 1st International Workshop on Security and Privacy for the Internet-of-Things (IoTSec), Orlando, FL, USA, 2018.
- Fahim, 2019, Anomaly detection, analysis and prediction techniques in IoT environment: A systematic literature review, IEEE Access, 7, 81664, 10.1109/ACCESS.2019.2921912 · DOI
- Wu, 2020, LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT, IEEE Trans. Ind. Inf., 16, 5244, 10.1109/TII.2019.2952917 · DOI
- Fahim, 2019, Anomaly detection, analysis and prediction techniques in iot environment: A systematic literature review, IEEE Access, 7, 81664, 10.1109/ACCESS.2019.2921912 · DOI
- Yin, 2020, Anomaly detection based on convolutional recurrent autoencoder for IoT time series, IEEE Trans. Syst. Man Cybern. Syst., 1
- Shen, 2019, Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities, IEEE Internet Things J., 6, 7702, 10.1109/JIOT.2019.2901840 · DOI
- Cauteruccio, 2021, A framework for anomaly detection and classification in multiple IoT scenarios, Future Gener. Comput. Syst., 114, 322, 10.1016/j.future.2020.08.010 · DOI
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Gamal, A., Elfakharany, E., Youssif, A. A. A., & Emam, O. E. (2026) · IEEE International Conference on Smart Sustainable Systems for Computer and Engineering Applications (3SCEA) · doi:10.1109/3scea68071.2026.11603017
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Kilichev, D., & Kim, W. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102039
TAME: Topological Anomaly detection in Multivariate timE series
Gharami, K., Hakim, S. B., Akbaş, M. İ., & Moni, S. S. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102036
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Dubey, K., Batra, I., Bajpai, A., Malik, A., Alshara, M., Jan, T., Ahmad, N., & Khalil, R. (2026) · Engineering Reports · doi:10.1002/eng2.70975
Chen, Y., Mu, F., & Lang, P. (2026) · International Conference on Computer Intelligence and Software Engineering (CICSE) · doi:10.1109/cicse70286.2026.11620930
Rizvi, A. A., Mehta, P., Lohith, N., Pandya, A., & Krishnan, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28304-7_21
Donato, M. C. D., Corradini, F., & Re, B. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108747
Weibull Mixture Models with Context-Specific Outliers for IoT Intrusion Detection
Sallay, H. (2026) · Future Internet · doi:10.3390/fi18080413
Enhancing decision-making through real-time detection of diverse anomalies in data streams
Alotaibi, O., Tomy, S., & Pardede, E. (2025) · International Conference on Intelligent Computing and Its Emerging Applications (ICEA) · doi:10.1145/3803817.3803818
Rehman, K., & Akash, M. S. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45911-5.00009-x
IoT Detection and Classification Techniques: A Review of Algorithms and Applications
Mortatha, M. B., & Abdulah, D. A. (2026) · International Conference on Neural Networks and Neurotechnologies (NeuroNT) · doi:10.1109/neuront71829.2026.11651420
From detection to repair: an interactive framework for reliable IoT data streams
Alotaibi, O., Pardede, E., & Tomy, S. (2026) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-12-2025-0596
Abnormal Instruction Identification of AVC Based on Data-Driven Approach
Wang, B. (2026) · International Symposium on New Energy Technologies and Power Systems (NETPS) · doi:10.1109/netps70564.2026.11650338
Gharkan, M., Al-Janabi, M. I. H., & Salim, O. (2026) · Al-Noor Journal of Engineering Management and Computer Science · doi:10.71229/e60hg537
A benchmark for IoT-deployed visual anomaly detection under compression constraints
Stropeni, A., Genilotti, F., Borsatti, F., Barusco, M., Pezze, D. D., & Susto, G. A. (2026) · IFAC Journal of Systems and Control · doi:10.1016/j.ifacsc.2026.100444
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Paszkiewicz, A., Gołąbek, A., Sowa, P., Kozik, M., & Bolanowski, M. (2026) · Annals of Computer Science and Information Systems · doi:10.15439/2026f2726
Anomaly Detection Based on Reconstruction Error in High-Frequency Hydroponic Sensor Data
AĞGÜL, B., & Kaan, A. R. I. K. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1858708
Cross-Layer Intrusion Detection in IoT via Honeypot and Hardware Telemetry Fusion
Kaushikbhai, P. C., Parekh, P., & Hemamalini, V. (2026) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa69367.2026.11707957
Баенова, Г. М., Серикова, М., & Pavate, A. A. (2026) · IETI Transactions on Data Analysis and Forecasting (iTDAF) · doi:10.3991/itdaf.v4i3.61867
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Weijia, W., Bing, X., Xi, Y., & Shi, J. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01321-1
Pastor, D. S., Muñoz, L. A., Berná-Martínez, J. V., & Asensi, C. C. (2026) · Evolving Systems · doi:10.1007/s12530-026-09895-x
K.Chandrasekaran, Dhiravidamani, P., Kumar, R. A., Manogar.K, Prabu, M., & Naveen., P. (2026) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec70158.2026.11735854
Metadata
- Authors
- Chatterjee, A., & Ahmed, B. S.
- Affiliations
- Karlstad University (SE)
- Corresponding author
- Ahmed, Bestoun S.
- Venue
- Internet of Things
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 19
- Pages
- 100568
- ISSN
- 2542-6605 (ISSN-L)
- OpenAlex
- W4285082635
- Cited by
- 323
- References
- 120
- Citations by year
- 2026: 70 · 2025: 100 · 2024: 99 · 2023: 51 · 2022: 3
- FWCI
- 20.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Internet of Things (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- 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
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- IoT anomaly detection, anomaly detection algorithms, Internet of Things security, sensor-based monitoring, home automation, smart cities, data drift, concept drift, data augmentation, ground truth, multi-sensor integration
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