The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey
Open accessAbstract
With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,414
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
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
Stuxnet: Dissecting a cyberwarfare weapon
Langner, R. (2011) · IEEE Security & Privacy Magazine · doi:10.1109/msp.2011.67 · Cited by 1,922
Wild patterns: Ten years after the rise of adversarial machine learning
Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,409
Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks
Fawzi, H., Tabuada, P., & Diggavi, S. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2303233 · Cited by 1,278
Cited by
Explainable Artificial Intelligence in CyberSecurity: A Survey
Capuano, N., Fenza, G., Loia, V., & Stanzione, C. (2022) · IEEE Access · doi:10.1109/access.2022.3204171 · Cited by 330
“In the sphere of CyberSecurity, researchers apply DL techniques for a variety of applications such as detecting network intrusions, malware traffic detec-471 tion and classification, and so on, as analyzed extensively in [55], [56], [57], and [58].”
MethodDetecting Vulnerability on IoT Device Firmware: A Survey
Feng, X., Zhu, X., Han, Q.-L., Zhou, W., Wen, S., & Xiang, Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105860 · Cited by 185
Self-citationA Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems
Jeffrey, N., Tan, Q., & Villar, J. R. (2023) · Electronics · doi:10.3390/electronics12153283 · Cited by 160
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
Gaba, S., Budhiraja, I., Kumar, V., Martha, S., Khurmi, J., Singh, A., Singh, K. K., Askar, S. S., & Abouhawwash, M. (2024) · IEEE Access · doi:10.1109/access.2023.3349022 · Cited by 62
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Qi, M., Wang, Z., Han, Q.-L., Zhang, J., Chen, S., & Xiang, Y. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.106058 · Cited by 44
Self-citation
Metadata
- Authors
- Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y.
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2022-03
- Volume
- 9
- Issue
- 3
- Pages
- 377-391
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W3200780444
- Cited by
- 432
- References
- 67
- FWCI
- 32.25
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey. https://app.cochat.ai/papers/10.1109/jas.2021.1004261
- The paper
- Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y. (2022). Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey. IEEE/CAA Journal of Automatica Sinica. https://doi.org/10.1109/jas.2021.1004261
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
