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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“The team in this project [130] has created the Nuru app to aid farmers in identifying and combating plant diseases.”
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“PlantVillage [130] is one of these chances.It is an open-source project managed by Penn State University.”
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“By using ML through TensorFlow Lite on mobile phones, the app provides real-time solutions without internet access, which is crucial for farmers in remote areas.”
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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
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
Inuwa, M. M., & Daş, R. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101162 · Cited by 191
A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges
Sasi, T., Lashkari, A. H., Lu, R., Xiong, P., & Iqbal, S. (2023) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2023.12.001 · Cited by 196
Rajagopal, S. M., Supriya, M., & Buyya, R. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100784 · Cited by 162
Geetha, R., Jegatheesan, A., Dhanaraj, R. K., Vijayalakshmi, K., Nayyar, A., Arulkumar, V., Velmurugan, J., & Thavasimuthu, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19617-7 · Cited by 127
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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