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Joint attack detection and secure state estimation of cyber‐physical systems
Open accessAbstract
Summary: This paper deals with secure state estimation of cyber‐physical systems subject to switching (on/off) attack signals and injection of fake packets (via either packet substitution or insertion of extra packets). The random set paradigm is adopted in order to model, via random finite sets (RFSs), the switching nature of both system attacks and the injection of fake measurements. The problem of detecting an attack on the system and jointly estimating its state, possibly in the presence of fake measurements, is then formulated and solved in the Bayesian framework for systems with and without direct feedthrough of the attack input to the output. This leads to the analytical derivation of a hybrid Bernoulli filter (HBF) that updates in real time the joint posterior density of a Bernoulli attack RFS and of the state vector. A closed‐form Gaussian mixture implementation of the proposed HBF is fully derived in the case of invertible direct feedthrough. Finally, the effectiveness of the developed tools for joint attack detection and secure state estimation is tested on two case studies concerning a benchmark system for unknown input estimation and a standard IEEE power network application.
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Citations
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Soldi, G., Gaglione, D., Forti, N., Millefiori, L. M., Braca, P., Carniel, S., Simone, A. D., Iodice, A., Riccio, D., Daffina, F. C., et al. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2021.3070884 · Cited by 44
“Based on recent advances on hybrid Bernoulli filtering [37], which combines standard Figure 6.”
MethodSelf-citationAnomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters
Forti, N., Millefiori, L. M., Braca, P., & Willett, P. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683428 · Cited by 29
Self-citationHybrid Bernoulli Filtering for Detection and Tracking of Anomalous Path Deviations
Forti, N., Millefiori, L. M., & Braca, P. (2018) · 2018 21st International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455567 · Cited by 14
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Metadata
- Authors
- Forti, N., Battistelli, G., Chisci, L., & Sinopoli, B.
- Affiliations
- NATO Centre for Maritime Research and Experimentation (IT)
- University of Florence (IT)
- Washington University in St. Louis (US)
- Corresponding author
- Forti, Nicola
- Venue
- International Journal of Robust and Nonlinear Control
- Publisher
- Wiley
- Published
- 2019-08-29
- Volume
- 30
- Issue
- 11
- Pages
- 4303-4330
- ISSN
- 1049-8923 (ISSN-L)
- 1099-1239
- OpenAlex
- W2567444260
- Cited by
- 29
- References
- 34
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 2 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 1
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Journal of Robust and Nonlinear Control (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CCF 1646526)
- U.S. Department of Energy (DE-OE0000779)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- attack detection, secure state estimation, cyber-physical systems, packet injection, random finite sets, Bayesian filtering, direct feedthrough, Gaussian mixture model, unknown input estimation
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