The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Hybrid Intrusion Detection System for Edge-Based IIoT Relying on Machine-Learning-Aided Detection
Abstract
The design of an intrusion detection system (IDS) plays a critical role in guaranteeing the security of the Industrial Internet of Things (IIoT). Recently, the rapid development of edge-based IIoT has posed new challenges in the design of a beneficial IDS considering its billions of IIoT devices and substantially decentralized data interaction. Both the detection method and the system architecture become the key components in constructing an efficient IDS for edge-based IIoT. In this article, we survey the typical state-of-theart studies about detection methods and system structure of IDS. Moreover, we propose a hybrid IDS architecture and introduce a machine learning aided detection method, which outperforms the literature in terms of a range of benchmarks.
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“The swift expansion of the IIoT has occurred simultaneously with the rise of cyber threats targeting critical infrastructure, such as smart factories and grids [106].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [106]
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Cites
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Self-citationLearning-Based Computation Offloading for IoT Devices With Energy Harvesting
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Deep Android Malware Detection
McLaughlin, N., del Rincón, J. M., Kang, B., Yerima, S. Y., Miller, P. C., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., Doupé, A., et al. (2017) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3029806.3029823 · Cited by 515
Routing Attacks and Countermeasures in the RPL-Based Internet of Things
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Intrusion Detection using Naive Bayes Classifier with Feature Reduction
Mukherjee, S., & Sharma, N. (2012) · Procedia Technology · doi:10.1016/j.protcy.2012.05.017 · Cited by 477
Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective
Wang, K., Wang, Y., Sun, Y., Guo, S., & Wu, J. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.1600399cm · Cited by 435
Satellite-5G Integration: A Network Perspective
Giambene, G., Kota, S., & Pillai, P. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800037 · Cited by 341
Security in Mobile Edge Caching with Reinforcement Learning
Xiao, L., Wan, X., Dai, C., Du, X., Chen, X., & Guizani, M. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700291 · Cited by 228
A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
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A hybrid network intrusion detection technique using random forests
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Cited by
Sorted by citations
Tange, K., De Donno, M., Fafoutis, X., & Dragoni, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011208 · Cited by 410
A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things
Zhao, R., Gui, G., Xue, Z., Yin, J., Ohtsuki, T. O., Adebisi, B., & Gacanin, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119055 · Cited by 230
Zhao, R., Zhan, M., Deng, X., Wang, Y., Wang, Y., Gui, G., & Xue, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25674 · Cited by 212
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
Macas, M., Wu, C., & Fuertes, W. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109032 · Cited by 202
Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things
Zhao, R., Wang, Y., Xue, Z., Ohtsuki, T. O., Adebisi, B., & Gui, G. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3175918 · Cited by 152
Metadata
- Authors
- Yao, H., Gao, P., Zhang, P., Wang, J., Jiang, C., & Lu, L.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- China University of Petroleum, East China (CN)
- Tsinghua University (CN)
- Venue
- IEEE Network
- Published
- 2019-09
- Volume
- 33
- Issue
- 5
- Pages
- 75-81
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2980185226
- Cited by
- 113
- References
- 15
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 16 · 2023: 31 · 2022: 16 · 2021: 17 · 2020: 7
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, Industrial Internet of Things, edge computing, machine learning for intrusion detection, IIoT security
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