Coding Schemes for Securing Cyber-Physical Systems Against Stealthy Data Injection Attacks
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Abstract (arXiv)
This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks. An intelligent attacker can design a sequence of data injection to sensors and actuators that pass the state estimator and statistical fault detector, based on knowledge of the system parameters. To stay undetected, the injected data should increase the state estimation errors while keep the estimation residues small. We employ a coding matrix to change the original sensor outputs to increase the estimation residues under intelligent data injection attacks. This is a low cost method compared with encryption schemes over all sensor measurements in communication networks. We show the conditions of a feasible coding matrix under the assumption that the attacker does not have knowledge of the exact coding matrix. An algorithm is developed to compute a feasible coding matrix, and, we show that in general, multiple feasible coding matrices exist. To defend against attackers who estimates the coding matrix via sensor and actuator measurements, time-varying coding matrices are designed according to the detection requirements. A heuristic algorithm to decide the time length of updating a coding matrix is then proposed.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2016.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
- 2021Learning-Based Vulnerability Analysis of Cyber-Physical Systemscited by 1
- 2022Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulationcited by 1
- 2022Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation11 Mar 2022
- 2021Learning-Based Vulnerability Analysis of Cyber-Physical Systems10 Mar 2021
- 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS14 Feb 2021