A saturated dynamic input allocation policy for preventing undetectable attacks in cyber-physical systems

The design of a dynamic allocator is proposed to tackle the presence of undetectable attacks in cyber-physical systems. Generating invisible signals that are capable to maintain the control inputs proximal to the saturation limits, the attacks are naturally discovered due to constraint violation. Two different design schemes are proposed: the first one is a direct allocation method based on a simple linear programming algorithm, while the second one is a dynamic optimization problem with a barrier function. The case-study of a consensus network illustrates the applicability and the efficacy of the proposed policy.

[1]  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.

[2]  Fei Hu,et al.  Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter , 2014, IEEE Transactions on Control of Network Systems.

[3]  Tor Arne Johansen,et al.  Fault tolerant control allocation using unknown input observers , 2014, Autom..

[4]  G. Basile,et al.  Controlled and conditioned invariants in linear system theory , 1992 .

[5]  Jean-François Tregouet,et al.  Globally Monotonic Tracking Control of Multivariable Systems , 2014, IEEE Transactions on Automatic Control.

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

[7]  Ilya Kolmanovsky,et al.  Discrete-time reference governors for systems with state and control constraints and disturbance inputs , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[8]  Francesco Bullo,et al.  Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems , 2015, IEEE Control Systems.

[9]  W. Marsden.  I and J , 2012 .

[10]  Karl Henrik Johansson,et al.  Cyberphysical Security in Networked Control Systems: An Introduction to the Issue , 2015 .

[11]  Ling Shi,et al.  Optimal Denial-of-Service Attack Scheduling With Energy Constraint , 2015, IEEE Transactions on Automatic Control.

[12]  Maria Letizia Corradini,et al.  Robust detection and reconstruction of state and sensor attacks for cyber-physical systems using sliding modes , 2017 .

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

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

[15]  André Teixeira,et al.  Networked control systems under cyber attacks with applications to power networks , 2010, Proceedings of the 2010 American Control Conference.

[16]  Hao Ye,et al.  Fault diagnosis of networked control systems , 2007, Annu. Rev. Control..

[17]  Ilya V. Kolmanovsky,et al.  Nonlinear tracking control in the presence of state and control constraints: a generalized reference governor , 2002, Autom..

[18]  Jill Slay,et al.  Lessons Learned from the Maroochy Water Breach , 2007, Critical Infrastructure Protection.

[19]  Rolf Isermann,et al.  Fault-Diagnosis Systems , 2005 .

[20]  Panos J. Antsaklis,et al.  Linear Systems , 1997 .

[21]  Silvia Pettinari,et al.  On dynamic input allocation for fat plants subject to multi-sinusoidal exogenous inputs , 2014, 53rd IEEE Conference on Decision and Control.

[22]  Sergio Galeani,et al.  Nonlinear regulation for linear fat plants: The constant reference/disturbance case , 2013, 21st Mediterranean Conference on Control and Automation.

[23]  Michael I. Jordan,et al.  Gradient Descent Only Converges to Minimizers , 2016, COLT.

[24]  Alberto Bemporad,et al.  Nonlinear predictive reference governor for constrained control systems , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[25]  W. M. Wonham,et al.  Linear Multivariable Control , 1979 .

[26]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[27]  Robert Schmid,et al.  A unified method for the design of nonovershooting linear multivariable state-feedback tracking controllers , 2010, Autom..

[28]  Luca Zaccarian,et al.  Dynamic allocation for input redundant control systems , 2009, Autom..

[29]  A. Serrani,et al.  Output regulation for over-actuated linear systems via inverse model allocation , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[30]  Andrea Cristofaro,et al.  Output invisible control allocation with steady-state input optimization for weakly redundant plants , 2014, 53rd IEEE Conference on Decision and Control.

[31]  Harry L. Trentelman,et al.  Control theory for linear systems , 2002 .

[32]  Alberto Bemporad,et al.  Reference governor for constrained nonlinear systems , 1998, IEEE Trans. Autom. Control..