Toward Optimal False Data Injection Attack against Self‐Triggered Model Predictive Controllers. Issue 6 (13th March 2022)
- Record Type:
- Journal Article
- Title:
- Toward Optimal False Data Injection Attack against Self‐Triggered Model Predictive Controllers. Issue 6 (13th March 2022)
- Main Title:
- Toward Optimal False Data Injection Attack against Self‐Triggered Model Predictive Controllers
- Authors:
- He, Ning
Ma, Kai
Li, Ruoxia - Abstract:
- Abstract: This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC) systems with sample‐and‐hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy. Abstract : This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC). The proposed method can effectively generate the worst FDI attack that destabilize the targeted system with a negligibly small amount of online computation, which help address the potential security defects of the STMPC system for generating effectiveAbstract: This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC) systems with sample‐and‐hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy. Abstract : This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC). The proposed method can effectively generate the worst FDI attack that destabilize the targeted system with a negligibly small amount of online computation, which help address the potential security defects of the STMPC system for generating effective countermeasures. … (more)
- Is Part Of:
- Advanced theory and simulations. Volume 5:Issue 6(2022)
- Journal:
- Advanced theory and simulations
- Issue:
- Volume 5:Issue 6(2022)
- Issue Display:
- Volume 5, Issue 6 (2022)
- Year:
- 2022
- Volume:
- 5
- Issue:
- 6
- Issue Sort Value:
- 2022-0005-0006-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2022-03-13
- Subjects:
- false data injection attack -- model predictive control -- networked control systems -- self‐triggered mechanisms
Science -- Simulation methods -- Periodicals
Science -- Methodology -- Periodicals
Engineering -- Simulation methods -- Periodicals
Engineering -- Methodology -- Periodicals
507.21 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/adts.202200025 ↗
- Languages:
- English
- ISSNs:
- 2513-0390
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.935575
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21821.xml