Fabio Pasqualetti and Florian Dorfler
Center for Control, Dynamical Systems and Computation, UC Santa Barbara
Cyber-physical systems integrate computation, communication, and physical capabilities. Examples of cyber-physical systems include transportation networks, power generation and distribution networks, water and gas distribution networks, and advanced communication systems. Because of the crucial role of cyber-physical systems in everyday life, cyber-physical security challenges need to be promptly addressed.
We propose a unified framework to analyze the resilience of cyber-physical systems against attacks cast by an omniscient adversary. We model cyber-physical systems as linear descriptor systems, and attacks as exogenous unknown inputs. Despite its simplicity, our model captures various real-world cyber-physical systems and it includes and generalizes the most studied prototypical attacks, including stealth, false-data injection and replay attacks. For this model, we study various attack detection and identification procedures, and we characterize their fundamental limitations. We provide constructive algebraic conditions to cast undetectable and unidentifiable attacks, and graph-theoretic conditions for the existence of undetectable and unidentifiable attacks.
Following our analysis, we propose centralized and decentralized monitors for attack detection and identification. To be specific, we first design centralized attack detection and identification monitors based upon the geometric notion of conditioned invariant subspace. In a second design step we develop a fully distributed attack detection filter based upon decentralized control methods and iterative local computations using the Gauss-Jacobi waveform relaxation technique. Finally, we show that the attack identification problem is inherently computationally hard, and we design a distributed identification method that achieves identification, at a low computational cost and for a certain class of attacks. Our distributed identification methods is based upon a divide and conquer procedure, in which first corrupted regions and then corrupted components are identified via local identification procedures and cooperation among neighboring regions.
Finally, we present several illustrative examples that illustrate our findings, and show the effectiveness of our methods also in the presence of system noise, nonlinearities, and modeling uncertainties.
Fabio Pasqualetti is a Doctoral Candidate in Mechanical Engineering at the University of California, Santa Barbara. He received the Laurea degree ``summa cum laude'' in Computer Engineering from the University of Pisa, Pisa, Italy, in 2004, and the Laurea Magistrale degree ``summa cum laude'' in Automation Engineering from the University of Pisa, Pisa, Italy, in 2007. His main research interest is in secure control systems, with application to multi-agent networks, distributed computing and power networks. Other interests include vehicle routing and combinatorial optimization, with application to distributed area patrolling and persistent surveillance.
Florian Dorfler is a Doctoral Candidate in Mechanical Engineering at the University of California, Santa Barbara. He received his Diplom degree in Engineering Cybernetics from the University of Stuttgart, Germany, in 2008. He was a visiting graduate researcher at the University of Toronto in 2007 and 2008, a corporal research intern at EADS Astrium in 2008, and graduate research intern at Los Alamos National Labs in 2011. His main research interests are distributed and coordinated control with applications in smart power grids and robotic networks. He is recipient of the 2009 Regents Special International Fellowship, the 2011 Peter J. Frenkel Foundation Fellowship, the 2010 ACC Student Best Paper Award, and the 2011 O. Hugo Schuck Best Paper Award.