Lasso-based state estimation for cyber-physical systems under sensor attacks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913370899218432 |
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| author | Cerone, Vito Fosson, Sophie M. Regruto, Diego Ripa, Francesco |
| author_facet | Cerone, Vito Fosson, Sophie M. Regruto, Diego Ripa, Francesco |
| contents | The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of the system.
In this paper, we propose an alternative, Lasso-based approach and we analyse its effectiveness. In particular, we theoretically derive conditions that guarantee successful attack/state recovery, independently of established time sparsity patterns. Furthermore, we develop a sparse state observer, by starting from the iterative soft thresholding algorithm for Lasso, to perform online estimation.
Through several numerical experiments, we compare the proposed methods to the state-of-the-art algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lasso-based state estimation for cyber-physical systems under sensor attacks Cerone, Vito Fosson, Sophie M. Regruto, Diego Ripa, Francesco Optimization and Control Systems and Control The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of the system. In this paper, we propose an alternative, Lasso-based approach and we analyse its effectiveness. In particular, we theoretically derive conditions that guarantee successful attack/state recovery, independently of established time sparsity patterns. Furthermore, we develop a sparse state observer, by starting from the iterative soft thresholding algorithm for Lasso, to perform online estimation. Through several numerical experiments, we compare the proposed methods to the state-of-the-art algorithms. |
| title | Lasso-based state estimation for cyber-physical systems under sensor attacks |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2405.20209 |