The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems
Open accessAbstract
Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.
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Cites
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., & Jain, R. K. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2912022 · Cited by 526
Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks
Yu, J. J. Q., Hou, Y., & Li, V. O. K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2825243 · Cited by 386
Zhang, F., Kodituwakku, H. A. D. E., Hines, J. W., & Coble, J. B. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2891261 · Cited by 371
A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
Aboelwafa, M., Seddik, K. G., Eldefrawy, M. H., Gadallah, Y., & Gidlund, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991693 · Cited by 205
Noor, U., Anwar, Z., Amjad, T., & Choo, K. R. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.013 · Cited by 199
HML-IDS: A Hybrid-Multilevel Anomaly Prediction Approach for Intrusion Detection in SCADA Systems
Khan, I. A., Pi, D., Khan, Z. U., Hussain, Y., & Nawaz, A. (2019) · IEEE Access · doi:10.1109/access.2019.2925838 · Cited by 165
Industrial Control System Network Intrusion Detection by Telemetry Analysis
Ponomarev, S., & Atkison, T. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2443793 · Cited by 147
IIoT cybersecurity risk modeling for SCADA systems
Falco, G., Caldera, C., & Shrobe, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2822842 · Cited by 137
Yang, J. Y., Zhou, C., Yang, S., Xu, H., & Hu, B. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2772190 · Cited by 127
Research works printed but not linked: 1
- J. F. Clemente, "No cyber security for critical energy infrastructure," Ph.D. dissertation, Naval Postgraduate School, Monterey, CA, USA, 2018.
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Sorted by citations
Zhukabayeva, T. K., Zholshiyeva, L., Karabayev, N., Khan, S., & Alnazzawi, N. (2025) · Sensors · doi:10.3390/s25010213 · Cited by 144
Sobb, T., Moustafa, N., & Turnbull, B. (2025) · Future Internet · doi:10.3390/fi17070282 · Cited by 14
Alrslani, F. A. F., Alohali, M. A., Aljebreen, M., Alqahtani, H., Alshuhail, A., Alshammeri, M., & ALMUKADI, W. S. (2025) · Scientific Reports · doi:10.1038/s41598-024-82566-6 · Cited by 13
Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15
Mitigating cyberphysical risks in IoT-enabled smart transport infrastructure
Alkhudhayr, H., & Ardah, H. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06948-x · Cited by 7
Metadata
- Authors
- Atheeq, C., Sultana, R., Sabahath, S. A., & Mohammed, M. A. K.
- Affiliations
- GITAM University (IN)
- King Khalid University (SA)
- Corresponding author
- Atheeq, C.
- ORCID
- Atheeq, C.
- Venue
- Engineering, Technology & Applied Science Research
- Published
- 2024-04-02
- Volume
- 14
- Issue
- 2
- Pages
- 13559-13566
- ISSN
- 1792-8036 (ISSN-L)
- 2241-4487
- OpenAlex
- W4393910828
- Cited by
- 18
- References
- 24
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Engineering Technology & Applied Science Research (publishedVersion, cc-by)
- Greater South Information System
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- Internet of Things security, cyber-physical systems, industrial control systems, attack detection, attack attribution, ensemble deep learning, decision tree, class imbalance, water purification, gas pipelines
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- CoChat. (2026). Citation check: Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems. https://app.cochat.ai/papers/10.48084/etasr.6969
- The paper
- Atheeq, C., Sultana, R., Sabahath, S. A., & Mohammed, M. A. K. (2024). Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems. Engineering, Technology & Applied Science Research. https://doi.org/10.48084/etasr.6969
