The DOI matches its Crossref record; 108 of the 112 references it cites were found in a registry or an index.
A Survey of Physics-Based Attack Detection in Cyber-Physical Systems
Open accessAbstract
Monitoring the "physics" of cyber-physical systems to detect attacks is a growing area of research. In its basic form a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements in order to identify potentially false control commands or false sensor readings. In this paper, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges, and propose new solutions.
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“The focus of most of the surveys for CPS is on security and privacy (e.g., [84,85,93,96,102,112,113,119,124]).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [85]
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“Unfortunately, IoT devices and networks are known to be highly vulnerable to security attacks that target data integrity and service availability.”
From Deep learning-enabled anomaly detection for IoT systems (2023) · cites this paper as [6]
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Citations
Cites
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Cited by
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Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges
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Fake news detection: A hybrid CNN-RNN based deep learning approach
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100007 · Cited by 568
A systems and control perspective of CPS security
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Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491
Metadata
- Authors
- Giraldo, J., Urbina, D., Cardenas, A., Valente, J., Faisal, M., Ruths, J., Tippenhauer, N. O., Sandberg, H., & Candell, R.
- Affiliations
- The University of Texas at Dallas (US)
- University of Texas at Dallas, Richardson, USA
- Singapore University of Technology and Design (SG)
- KTH Royal Institute of Technology (SE)
- National Institute of Standards and Technology (US)
- Venue
- ACM Computing Surveys
- Published
- 2018-07-25
- Volume
- 51
- Issue
- 4
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- PMC
- PMC6512826
- OpenAlex
- W2803355698
- Cited by
- 535
- References
- 112
- Citations by year
- 2026: 33 · 2025: 54 · 2024: 52 · 2023: 65 · 2022: 80 · 2021: 125 · 2020: 82 · 2019: 43 · 2018: 1
- FWCI
- 38.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACM Computing Surveys (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NRF Singapore (NRF2014NCRNCR001-40)
- Swedish Research Council (2013-5523)
- NIST (70NANB16H019 and 70NANB17H282)
- NSF (CNS-1553683 and CNS-1718848)
- Swedish Civil Contingencies Agency
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- cyber-physical systems, attack detection, industrial control systems, sensor readings, time series models, security monitoring
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