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]
Citations
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
Le, A., Loo, J. K. K., Chai, K. K., & Aiash, M. (2016) · Information · doi:10.3390/info7020025 · Cited by 205
A hybrid network intrusion detection technique using random forests
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Divya, K. V., & Khanna, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6906-6_25
Renuka, K., Chintalapudi, S. R., Tiwari, R., & Shabari, S. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9245-3_37
Machine and Deep Learning Applications in IoT: A Systematic Literature Review
Diaz, J. M. B., & Jabba, D. (2025) · IEEE Colombian Caribbean Conference (C3) · doi:10.1109/c366505.2025.11340541
Kumar, A., Kaur, S., Sachdeva, R., & Kaur, K. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362512
Sanjalawe, Y. K., Fraihat, S., Al-E’mari, S., & Makhadmeh, S. N. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2026.03.002
Yue, H., Zhang, H., Xiao, X., Yu, L., Hu, G., & Li, Q. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460777
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Chen, G., Liu, Y., & Yu, L. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-22545-0_1
Multi-Class Network Intrusion Detection Using Flow-Based Features: A Machine Learning Approach
Isyaku, B., Garba, S., Faruk, N., & Alhassan, I. M. (2025) · IEEE Connecting the Unconnected Europe, Middle East, and Africa Regional Summit (CTUS-EMEA) · doi:10.1109/ctus-emea68989.2025.11592252
Latif, S., Javed, H., & Shaheen, S. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1909
A hybrid CNN and attentive hierarchical BiLSTM model with SMO for intrusion detection in IIoT
Pawar, S. L., & Karyakarte, M. S. (2026) · Discover Internet of Things · doi:10.1007/s43926-025-00208-3
Ahmed, S., Hunsikatti, S. S., Raj, S. M., Sagar, D. V., & Pavithra, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-38800-1_20
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Jamshidi, S., Nikanjam, A., Wazed, N. K., & Khomh, F. (2026) · Springer handbooks · doi:10.1007/978-3-031-92372-2_4
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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