Model-free predictive control of nonlinear systems under False Data Injection attacks
The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attack...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Zeyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver - Couch, Yvonne ELSEVIER, 2014, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:100 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.compeleceng.2022.107977 |
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ELV057923787 |
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| 520 | |a The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. | ||
| 520 | |a The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. | ||
| 650 | 7 | |a Volterra series |2 Elsevier | |
| 650 | 7 | |a False data injection attack |2 Elsevier | |
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| 650 | 7 | |a Model-free predictive control |2 Elsevier | |
| 650 | 7 | |a Polynomial regression |2 Elsevier | |
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| 700 | 1 | |a Zhang, Heng |4 oth | |
| 700 | 1 | |a Zhang, Jian |4 oth | |
| 700 | 1 | |a Zhong, Zhaoman |4 oth | |
| 700 | 1 | |a Xu, Weiwei |4 oth | |
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10.1016/j.compeleceng.2022.107977 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001790.pica (DE-627)ELV057923787 (ELSEVIER)S0045-7906(22)00251-8 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zhang, Zeyu verfasserin aut Model-free predictive control of nonlinear systems under False Data Injection attacks 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Volterra series Elsevier False data injection attack Elsevier Nonlinear predictive control systems Elsevier Model-free predictive control Elsevier Polynomial regression Elsevier Li, Hongran oth Zhang, Heng oth Zhang, Jian oth Zhong, Zhaoman oth Xu, Weiwei oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:100 year:2022 pages:0 https://doi.org/10.1016/j.compeleceng.2022.107977 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 100 2022 0 |
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10.1016/j.compeleceng.2022.107977 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001790.pica (DE-627)ELV057923787 (ELSEVIER)S0045-7906(22)00251-8 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zhang, Zeyu verfasserin aut Model-free predictive control of nonlinear systems under False Data Injection attacks 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Volterra series Elsevier False data injection attack Elsevier Nonlinear predictive control systems Elsevier Model-free predictive control Elsevier Polynomial regression Elsevier Li, Hongran oth Zhang, Heng oth Zhang, Jian oth Zhong, Zhaoman oth Xu, Weiwei oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:100 year:2022 pages:0 https://doi.org/10.1016/j.compeleceng.2022.107977 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 100 2022 0 |
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10.1016/j.compeleceng.2022.107977 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001790.pica (DE-627)ELV057923787 (ELSEVIER)S0045-7906(22)00251-8 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zhang, Zeyu verfasserin aut Model-free predictive control of nonlinear systems under False Data Injection attacks 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Volterra series Elsevier False data injection attack Elsevier Nonlinear predictive control systems Elsevier Model-free predictive control Elsevier Polynomial regression Elsevier Li, Hongran oth Zhang, Heng oth Zhang, Jian oth Zhong, Zhaoman oth Xu, Weiwei oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:100 year:2022 pages:0 https://doi.org/10.1016/j.compeleceng.2022.107977 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 100 2022 0 |
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10.1016/j.compeleceng.2022.107977 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001790.pica (DE-627)ELV057923787 (ELSEVIER)S0045-7906(22)00251-8 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zhang, Zeyu verfasserin aut Model-free predictive control of nonlinear systems under False Data Injection attacks 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Volterra series Elsevier False data injection attack Elsevier Nonlinear predictive control systems Elsevier Model-free predictive control Elsevier Polynomial regression Elsevier Li, Hongran oth Zhang, Heng oth Zhang, Jian oth Zhong, Zhaoman oth Xu, Weiwei oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:100 year:2022 pages:0 https://doi.org/10.1016/j.compeleceng.2022.107977 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 100 2022 0 |
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10.1016/j.compeleceng.2022.107977 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001790.pica (DE-627)ELV057923787 (ELSEVIER)S0045-7906(22)00251-8 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zhang, Zeyu verfasserin aut Model-free predictive control of nonlinear systems under False Data Injection attacks 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Volterra series Elsevier False data injection attack Elsevier Nonlinear predictive control systems Elsevier Model-free predictive control Elsevier Polynomial regression Elsevier Li, Hongran oth Zhang, Heng oth Zhang, Jian oth Zhong, Zhaoman oth Xu, Weiwei oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:100 year:2022 pages:0 https://doi.org/10.1016/j.compeleceng.2022.107977 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 100 2022 0 |
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Enthalten in Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver Amsterdam [u.a.] volume:100 year:2022 pages:0 |
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Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver |
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Model-free predictive control of nonlinear systems under False Data Injection attacks |
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The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. |
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The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. |
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The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. |
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