Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach
- DOI
- 10.1016/j.compind.2021.103509
- Published
- 2021-11
- Container
- Computers in Industry
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.compind.2021.103509,
title = {Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach},
author = {Truong Thu Huong and Ta Phuong Bac and Dao Minh Long and Tran Duc Luong and Nguyen Minh Dan and Le Anh Quang and Le Thanh Cong and Bui Doan Thang and Kim Phuc Tran},
year = {2021},
journal = {Computers in Industry},
doi = {10.1016/j.compind.2021.103509},
url = {https://doi.org/10.1016/j.compind.2021.103509}
}RIS
TY - JOUR TI - Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach AU - Truong Thu Huong AU - Ta Phuong Bac AU - Dao Minh Long AU - Tran Duc Luong AU - Nguyen Minh Dan AU - Le Anh Quang AU - Le Thanh Cong AU - Bui Doan Thang AU - Kim Phuc Tran PY - 2021 JO - Computers in Industry DO - 10.1016/j.compind.2021.103509 UR - https://doi.org/10.1016/j.compind.2021.103509 ER -
APA
Huong, T. T., Bac, T. P., Long, D. M., Luong, T. D., Dan, N. M., Quang, L. A., Cong, L. T., Thang, B. D., & Tran, K. P. (2021). Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach. Computers in Industry. https://doi.org/10.1016/j.compind.2021.103509
Source records
- crossref · retrieved 2026-09-27T13:09:02.175Z