Black-box System Identification of CPS Protected by a Watermark-based Detector
| Title | Black-box System Identification of CPS Protected by a Watermark-based Detector |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Guibene, K., Ayaida, M., Khoukhi, L., MESSAI, N. |
| Conference Name | 2020 IEEE 45th Conference on Local Computer Networks (LCN) |
| Keywords | black box encryption, black-box, composability, CPS Modeling and Simulation, Cyber-physical security, Detectors, Metrics, Networked Control System, Particle separators, Predictive Metrics, process control, pubcrawl, Resiliency, security, Sensors, Support vector machines, support vector regression, System Identification, Watermarking |
| Abstract | The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm. |
| DOI | 10.1109/LCN48667.2020.9314803 |
| Citation Key | guibene_black-box_2020 |
- Networked Control System
- Watermarking
- System Identification
- support vector regression
- Support vector machines
- sensors
- security
- pubcrawl
- process control
- Particle separators
- CPS Modeling and Simulation
- Metrics
- Detectors
- cyber-physical security
- black-box
- black box encryption
- Predictive Metrics
- composability
- Resiliency