Optimal ϵ -stealthy attack in cyber-physical systems

Abstract This paper is concerned with the optimal stealthy attack problems of cyber-physical system, which is represented as discrete-time linear systems. It is considered that a deceptive attack assumed to be able to hijack and modify the nominal control signals from the controller-actuator channel with the metric of ϵ -stealthiness. Different from the existing literatures, some optimal attack strategies with objectives such that maintaining a fixed level of stealthiness and achieving the maximized performance degradation are designed under some constraints. It is further derived that the results of ours are more relaxed than some methods proposed before. Finally, some numerical simulations are given to illustrate the validity of the theoretical results.

[1]  Fuad E. Alsaadi,et al.  Centralized security-guaranteed filtering in multirate-sensor fusion under deception attacks , 2018, J. Frankl. Inst..

[2]  Guang-Hong Yang,et al.  Secure State Estimation Against Sparse Sensor Attacks With Adaptive Switching Mechanism , 2018, IEEE Transactions on Automatic Control.

[3]  Xin Huang,et al.  Adaptive integral sliding‐mode control strategy of data‐driven cyber‐physical systems against a class of actuator attacks , 2018, IET Control Theory & Applications.

[4]  Ruochi Zhang,et al.  Stealthy control signal attacks in scalar LQG systems , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[5]  Jianying Zhou,et al.  NoisePrint: Attack Detection Using Sensor and Process Noise Fingerprint in Cyber Physical Systems , 2018, AsiaCCS.

[6]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2011, TSEC.

[7]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[8]  Gabriela Hug,et al.  Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks , 2012, IEEE Transactions on Smart Grid.

[9]  Paulo Tabuada,et al.  Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks , 2012, IEEE Transactions on Automatic Control.

[10]  Karl Henrik Johansson,et al.  A secure control framework for resource-limited adversaries , 2012, Autom..

[11]  Vijay Gupta,et al.  Security in stochastic control systems: Fundamental limitations and performance bounds , 2015, 2015 American Control Conference (ACC).

[12]  Stamatis Karnouskos,et al.  Stuxnet worm impact on industrial cyber-physical system security , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[13]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[14]  Ling Shi,et al.  The Performance and Limitations of $\epsilon$- Stealthy Attacks on Higher Order Systems , 2017, IEEE Transactions on Automatic Control.

[15]  Ling Shi,et al.  Optimal DoS Attack Scheduling in Wireless Networked Control System , 2016, IEEE Transactions on Control Systems Technology.

[16]  Ali H. Sayed,et al.  Linear Estimation (Information and System Sciences Series) , 2000 .

[17]  Panos J. Antsaklis,et al.  Goals and Challenges in Cyber-Physical Systems Research Editorial of the Editor in Chief , 2014, IEEE Trans. Autom. Control..

[18]  Xin Huang,et al.  Adaptive optimisation-offline cyber attack on remote state estimator , 2017, Int. J. Syst. Sci..

[19]  Changyin Sun,et al.  Optimal jamming attack schedule for remote state estimation with two sensors , 2018, J. Frankl. Inst..

[20]  Ling Shi,et al.  Optimal Attack Energy Allocation against Remote State Estimation , 2018, IEEE Transactions on Automatic Control.

[21]  Lamine Mili,et al.  A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures , 2018, IEEE Transactions on Power Systems.

[22]  Vijay Gupta,et al.  On Kalman Filtering with Compromised Sensors: Attack Stealthiness and Performance Bounds , 2017, IEEE Transactions on Automatic Control.

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[25]  Vijay Gupta,et al.  Data-injection attacks in stochastic control systems: Detectability and performance tradeoffs , 2017, Autom..

[26]  Guang-Hong Yang,et al.  A data-driven covert attack strategy in the closed-loop cyber-physical systems , 2018, J. Frankl. Inst..

[27]  Panganamala Ramana Kumar,et al.  Cyber–Physical Systems: A Perspective at the Centennial , 2012, Proceedings of the IEEE.

[28]  Guang-Hong Yang,et al.  Observer-Based Output Feedback Control for Discrete-Time T-S Fuzzy Systems With Partly Immeasurable Premise Variables , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Ling Shi,et al.  Optimal Linear Cyber-Attack on Remote State Estimation , 2017, IEEE Transactions on Control of Network Systems.

[30]  Tongwen Chen,et al.  False Data Injection Attacks on Networked Control Systems: A Stackelberg Game Analysis , 2018, IEEE Transactions on Automatic Control.

[31]  Guang-Hong Yang,et al.  Improved adaptive resilient control against sensor and actuator attacks , 2018, Inf. Sci..

[32]  Peter Xiaoping Liu,et al.  A stochastic game approach to the security issue of networked control systems under jamming attacks , 2014, J. Frankl. Inst..

[33]  Ding Zhai,et al.  Robust Adaptive Fuzzy Control of a Class of Uncertain Nonlinear Systems With Unstable Dynamics and Mismatched Disturbances , 2018, IEEE Transactions on Cybernetics.

[34]  Fatos Xhafa,et al.  Special issue on cyber physical systems , 2013, Computing.

[35]  Wei Yu,et al.  On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures , 2014, IEEE Transactions on Parallel and Distributed Systems.

[36]  Bruno Sinopoli,et al.  Integrity attacks on cyber-physical systems , 2012, HiCoNS '12.