Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods
Integrity signals
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Cites
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
These elaborate ML techniques are ineffective in performing dynamic responses for IoT.
“These elaborate techniques are ineffective, however, in performing dynamic responses as far as the IoT is concerned [131,132,133,134].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [133]
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References 27 · Cited by 238
- Authors
- Wu, M., Song, Z., & Moon, Y. B.
- Affiliations
- Syracuse University (US)
- Corresponding author
- Moon, Young B.
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2017-02-23
- Volume
- 30
- Issue
- 3
- Pages
- 1111-1123
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W2591507999
- Cited by
- 238
- References
- 27
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 30 · 2023: 33 · 2022: 47 · 2021: 25 · 2020: 30 · 2019: 17 · 2018: 17 · 2017: 5
- FWCI
- 12.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- cyber-physical attacks, attack detection, anomaly detection, attack taxonomy, 3D printing, CNC milling, physical data, random forest, Stuxnet, manufacturing cybersecurity
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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.
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