The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning
Abstract
The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.
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Cites
Industrial Internet of Things: Challenges, Opportunities, and Directions
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2852491 · Cited by 2,380
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232
DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
Li, B., Wu, Y., Song, J., Lu, R., Li, T., & Zhao, L. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3023430 · Cited by 597
Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., & Hossain, M. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3011726 · Cited by 541
Self-citationCommunication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436
Self-citationFederated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks
Savazzi, S., Nicoli, M., & Rampa, V. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2964162 · Cited by 431
Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning
Wang, J., Hu, J., Min, G., Zomaya, A. Y., & Georgalas, N. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3014896 · Cited by 414
Self-citationMumtaz, S., Alsohaily, A., Pang, Z., Rayes, A., Tsang, K. F., & Rodriguez, J. (2017) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2016.2618724 · Cited by 342
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
Wu, D., Jiang, Z., Xie, X., Wei, X., Yu, W., & Li, R. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2952917 · Cited by 290
Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things
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Cited by
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Internet of Things (IoT) security with blockchain technology: A state-of-the-art review
Ayub Khan, A., Laghari, A. A., Shaikh, Z. A., Dacko-Pikiewicz, Z., & Kot, S. (2022) · IEEE Access · doi:10.1109/access.2022.3223370 · Cited by 223
Kalapaaking, A. P., Khalil, I., & Yi, X. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3268186 · Cited by 122
Alahmari, S., & Alghamdi, I. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107849 · Cited by 21
Metadata
- Authors
- Wang, X., Garg, S., Lin, H., Hu, J., Kaddoum, G., Jalil Piran, M., & Hossain, M. S.
- Venue
- IEEE Internet of Things Journal
- Published
- 2022-05-15
- Volume
- 9
- Issue
- 10
- Pages
- 7110-7119
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3154459044
- Cited by
- 268
- References
- 20
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, X., Garg, S., Lin, H., Hu, J., Kaddoum, G., Jalil Piran, M., & Hossain, M. S. (2022). Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2021.3074382
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