Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks
In this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances. The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The a...
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| Veröffentlicht in: | Asian journal of control Jg. 24; H. 6; S. 3149 - 3162 |
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| Sprache: | Englisch |
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01.11.2022
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| ISSN: | 1561-8625, 1934-6093 |
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| Abstract | In this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances. The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The addressed hybrid cyber attacks consist of both denial‐of‐service (DoS) attacks and deception attacks, in which the injected signal obeys a Gaussian mixture distribution. With the help of Kalman filtering theory, an optimally distributed filtering scheme is developed and the desired filter gain is derived by minimizing the filtering error covariance at each time instant. Furthermore, when the statistical information on DoS attacks is not available, the states of DoS attacks are identified in real‐time by resorting to a maximum posterior probability estimation approach combined with the classical Bayesian rule. Based on the developed formulas and the identified state, a suboptimal distributed filtering algorithm is established to realize the state estimation. Finally, a simulation example shows the effectiveness of the proposed distributed resilient filtering algorithm. |
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| AbstractList | In this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances. The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The addressed hybrid cyber attacks consist of both denial‐of‐service (DoS) attacks and deception attacks, in which the injected signal obeys a Gaussian mixture distribution. With the help of Kalman filtering theory, an optimally distributed filtering scheme is developed and the desired filter gain is derived by minimizing the filtering error covariance at each time instant. Furthermore, when the statistical information on DoS attacks is not available, the states of DoS attacks are identified in real‐time by resorting to a maximum posterior probability estimation approach combined with the classical Bayesian rule. Based on the developed formulas and the identified state, a suboptimal distributed filtering algorithm is established to realize the state estimation. Finally, a simulation example shows the effectiveness of the proposed distributed resilient filtering algorithm. |
| Author | Ding, Derui Yi, Xiaojian Liu, Huanyi |
| Author_xml | – sequence: 1 givenname: Huanyi surname: Liu fullname: Liu, Huanyi organization: University of Shanghai for Science and Technology – sequence: 2 givenname: Derui orcidid: 0000-0001-7402-6682 surname: Ding fullname: Ding, Derui email: deruiding2010@usst.edu.cn organization: University of Shanghai for Science and Technology – sequence: 3 givenname: Xiaojian surname: Yi fullname: Yi, Xiaojian organization: Beijing Institute of Technology |
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| Cites_doi | 10.1080/00207721.2021.1885082 10.1002/asjc.2358 10.1109/TAC.2008.2010962 10.1109/TCNS.2021.3049361 10.1016/j.automatica.2016.12.026 10.1016/j.automatica.2012.03.027 10.1109/TAC.2016.2594378 10.1109/78.726807 10.1016/j.jfranklin.2013.02.006 10.1109/TSMC.2019.2960541 10.1109/TSMC.2019.2905253 10.1109/TSP.2016.2634541 10.1109/TAC.2004.834121 10.1049/iet-cta.2012.0732 10.1016/j.neucom.2015.06.072 10.1109/TSMC.2020.3041121 10.1109/TAC.2020.2989773 10.1109/TCSI.2018.2824306 10.1109/TCSI.2020.2986953 10.1016/j.neucom.2016.05.060 10.1016/j.automatica.2019.01.020 10.1109/TCNS.2020.3035759 10.1016/j.isatra.2019.02.025 10.1109/9.905707 10.1109/TNNLS.2013.2271357 10.1016/j.automatica.2015.06.037 10.1109/TCNS.2018.2802872 10.1002/rnc.5283 10.1016/j.inffus.2020.08.001 10.1080/00207721.2020.1868615 10.1109/TCYB.2019.2924450 10.1109/TAC.2010.2042987 10.1109/TII.2005.844422 10.1016/j.automatica.2006.10.023 10.1186/s13662-019-2298-7 10.1109/TCYB.2017.2789296 10.1002/asjc.1983 10.1109/TSP.2012.2200480 10.1002/asjc.1990 10.1002/0471221279 |
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| SubjectTerms | Algorithms Bayesian rule Conditional probability Cybersecurity Denial of service attacks discrete‐time nonlinear system distributed resilient filtering Dynamical systems hybrid cyber attacks Hybrid systems Kalman filters maximum posterior probability Nonlinear systems Normal distribution Perturbation State estimation Statistical analysis |
| Title | Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks |
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