Lasso-based state estimation for cyber-physical systems under sensor attacks
<|reference_start|>Lasso-based state estimation for cyber-physical systems under sensor attacks: 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.<|reference_end|>
@article{cerone2024lasso-based,
    title={Lasso-based state estimation for cyber-physical systems under sensor
  attacks},
    author={Vito Cerone and Sophie M. Fosson and Diego Regruto and Francesco Ripa},
    journal={arXiv preprint arXiv:2405.20209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20209},
    primaryClass={math.OC cs.SY eess.SY}
}