A Physics-Based Attack Detection Technique in Cyber-Physical Systems: A Model Predictive Control Co-Design Approach

In this paper a novel approach to co-design controller and attack detector for nonlinear cyber-physical systems affected by false data injection (FDI) attack is proposed. We augment the model predictive controller with an additional constraint requiring the future—in some steps ahead—trajectory of the system to remain in some time-invariant neighborhood of a properly designed reference trajectory. At any sampling time, we compare the real-time trajectory of the system with the designed reference trajectory, and construct a residual. The residual is then used in a nonparametric cumulative sum (CUSUM) anomaly detector to uncover FDI attacks on input and measurement channels. The effectiveness of the proposed approach is tested with a nonlinear model regarding level control of coupled tanks.

[1]  Vijay Gupta,et al.  On Kalman filtering in the presence of a compromised sensor: Fundamental performance bounds , 2014, 2014 American Control Conference.

[2]  Siddharth Sridhar,et al.  Cyber–Physical System Security for the Electric Power Grid , 2012, Proceedings of the IEEE.

[3]  Thomas Parisini,et al.  Model-based Detection of Cyber-Attacks in Networked MPC-based Control Systems , 2018 .

[4]  Michail Maniatakos,et al.  Security and Privacy in Cyber-Physical Systems: A Survey of Surveys , 2017, IEEE Design & Test.

[5]  Florian Dörfler,et al.  Attack Detection and Identification in Cyber-Physical Systems -- Part II: Centralized and Distributed Monitor Design , 2012, ArXiv.

[6]  G. Martin,et al.  Nonlinear model predictive control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[7]  Bruno Sinopoli,et al.  Networked Constrained Cyber-Physical Systems subject to malicious attacks: a resilient set-theoretic control approach , 2016, ArXiv.

[8]  Henrik Sandberg,et al.  A Survey of Physics-Based Attack Detection in Cyber-Physical Systems , 2018, ACM Comput. Surv..

[9]  Roy S. Smith,et al.  Covert Misappropriation of Networked Control Systems: Presenting a Feedback Structure , 2015, IEEE Control Systems.

[10]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[11]  Marcello Farina,et al.  Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems , 2012, Autom..

[12]  Sonia Martínez,et al.  On the Performance Analysis of Resilient Networked Control Systems Under Replay Attacks , 2013, IEEE Transactions on Automatic Control.

[13]  Mohamed Darouach,et al.  A model predictive approach for cyber-attack detection and mitigation in control systems , 2013, 52nd IEEE Conference on Decision and Control.

[14]  Bruno Sinopoli,et al.  Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs , 2015, IEEE Control Systems.

[15]  Yang Xiang,et al.  A survey on security control and attack detection for industrial cyber-physical systems , 2018, Neurocomputing.

[16]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[17]  Bruno Sinopoli,et al.  A set-theoretic approach for secure and resilient control of Cyber-Physical Systems subject to false data injection attacks , 2016, 2016 Science of Security for Cyber-Physical Systems Workshop (SOSCYPS).

[18]  Jorge Nocedal,et al.  An interior algorithm for nonlinear optimization that combines line search and trust region steps , 2006, Math. Program..

[19]  Nils Ole Tippenhauer,et al.  Cyber-Physical Systems Security Knowledge Area , 2019 .

[20]  Haibo He,et al.  Cyber-physical attacks and defences in the smart grid: a survey , 2016, IET Cyper-Phys. Syst.: Theory & Appl..

[21]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.