Data-driven Control Against False Data Injection Attacks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929375231868928 |
|---|---|
| author | Liu, Wenjie Li, Lidong Sun, Jian Deng, Fang Wang, Gang Chen, Jie |
| author_facet | Liu, Wenjie Li, Lidong Sun, Jian Deng, Fang Wang, Gang Chen, Jie |
| contents | The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-of-service (DoS) attacks and false data injection (FDI) attacks can be easily launched and have become prominent threats. While resilient control against DoS attacks has received substantial research efforts, countermeasures developed against FDI attacks have been relatively limited, particularly when explicit system models are not available. To address this gap, the present paper focuses on the design of data-driven controllers for unknown linear systems subject to FDI attacks on the actuators, utilizing input-state data. To this end, a general FDI attack model is presented, which imposes minimally constraints on the switching frequency of attack channels and the magnitude of attack matrices. A dynamic state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving two data-based semi-definite programs (SDPs) on-the-fly to yield a tight approximation of the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack, the proposed SDPs are recursively feasible and controller achieves exponential stability. Numerical examples showcase its effectiveness in mitigating the impact of FDI attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08207 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven Control Against False Data Injection Attacks Liu, Wenjie Li, Lidong Sun, Jian Deng, Fang Wang, Gang Chen, Jie Systems and Control The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-of-service (DoS) attacks and false data injection (FDI) attacks can be easily launched and have become prominent threats. While resilient control against DoS attacks has received substantial research efforts, countermeasures developed against FDI attacks have been relatively limited, particularly when explicit system models are not available. To address this gap, the present paper focuses on the design of data-driven controllers for unknown linear systems subject to FDI attacks on the actuators, utilizing input-state data. To this end, a general FDI attack model is presented, which imposes minimally constraints on the switching frequency of attack channels and the magnitude of attack matrices. A dynamic state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving two data-based semi-definite programs (SDPs) on-the-fly to yield a tight approximation of the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack, the proposed SDPs are recursively feasible and controller achieves exponential stability. Numerical examples showcase its effectiveness in mitigating the impact of FDI attacks. |
| title | Data-driven Control Against False Data Injection Attacks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2311.08207 |