Security in cyber-physical systems: Controller design against Known-Plaintext Attack

A substantial amount of research on the security of cyber-physical systems assumes that the physical system model is available to the adversary. In this paper, we argue that such an assumption can be relaxed, given that the adversary might still be able to identify the system model by observing the control input and sensory data from the system. In such a setup, the attack with the goal of identifying the system model using the knowledge of input-output data can be categorized as a Known-Plaintext Attack (KPA) in the information security literature. We first prove a necessary condition and a sufficient condition, under which the adversary can successfully identify the transfer function of the physical system. We then provide a low-rank controller design which renders the system unidentifiable to the adversary, while trading off the LQG performance.

[1]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[2]  Shreyas Sundaram,et al.  Distributed Function Calculation via Linear Iterative Strategies in the Presence of Malicious Agents , 2011, IEEE Transactions on Automatic Control.

[3]  B. Anderson The inverse problem of stationary covariance generation , 1969 .

[4]  Ye Yuan,et al.  Network Reconstruction from Intrinsic Noise , 2013, ArXiv.

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

[6]  Karl Henrik Johansson,et al.  Secure Control Systems: A Quantitative Risk Management Approach , 2015, IEEE Control Systems.

[7]  Bruno Sinopoli,et al.  Detecting Integrity Attacks on SCADA Systems , 2011 .

[8]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[9]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[10]  Keith Glover Structural Aspects of System Identification , 1973 .

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

[12]  Tung-Sang Ng,et al.  Identifiability of MIMO linear dynamic systems operating in closed loop , 1977, Autom..

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

[14]  Bruno Sinopoli,et al.  Foundations of Control and Estimation Over Lossy Networks , 2007, Proceedings of the IEEE.

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

[16]  W. Marsden I and J , 2012 .

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

[18]  David P. Fidler Was Stuxnet an Act of War? Decoding a Cyberattack , 2011, IEEE Security & Privacy.

[19]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[20]  Roy S. Smith,et al.  A Decoupled Feedback Structure for Covertly Appropriating Networked Control Systems , 2011 .

[21]  B. Anderson An algebraic solution to the spectral factorization problem , 1967, IEEE Transactions on Automatic Control.

[22]  Brian D. O. Anderson,et al.  Identifiability of linear stochastic systems operating under linear feedback , 1982, Autom..

[23]  Ali H. Sayed,et al.  A survey of spectral factorization methods , 2001, Numer. Linear Algebra Appl..

[24]  T. M. Chen,et al.  Stuxnet, the real start of cyber warfare? [Editor's Note] , 2010, IEEE Netw..