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NeCS
Networked Controlled System Team
Research Proposal – Ph.D. Thesis
Cyber-Physical Systems: a control-theoretic approach to privacy and security
Advisor: Alain Kibangou [email protected]
Co-advisor: Federica Garin [email protected]
Team: NeCS http://necs.inrialpes.fr, INRIA Rhône-Alpes, Grenoble (France)
Context:
NeCS is a joint team of INRIA and of Gipsa-lab (CNRS and Univ. Grenoble-Alpes). Its research focuses on Networked
Control Systems, and it involves the analysis and control of dynamical systems with a network structure or whose
operation is supported by networks, with particular attention for large-scale networks and cyber-physical systems. As a
main application domain, the team studies road traffic networks.
Candidate profile:
A master degree in engineering or applied mathematics is required. A strong background in automatic control and
systems theory is mandatory. Notions of graph theory and/or compressive sensing are appreciated.
Topic description:
Cyber-Physical Systems (CPSs) are systems in which physical processes are tightly intertwined with networked
computing, e.g., sensor networks, industrial automation systems, transportation networks, power generation and
distribution networks, water and gas distribution networks.
Usually, CPSs are large-scale systems, composed of many simple components (agents) with interconnections giving rise
to a global complex behaviour. Interesting recent research [1,2] has been exploring how the graph describing
interactions affects control-theoretic properties such as controllability or observability, namely answering the
question whether a small group of agents would be able to drive the whole system to a desired state, or to retrieve the state
of all agents from the observed local states only.
The study of observability is related to the understanding of privacy: from which local measurements is it possible to
reconstruct the state? If we impose that some information must remain private, can we design a system that achieves the
desired performance without disclosing any private information? Some preliminary works (e.g., [3]) consider the case
where the dynamical is some in-network computation (state evolution being numerical and not physical), and the private
information is the agent’s identifier. More scenarios are to be studied, involving physical systems.
A related problem is observability in the presence of an unknown input [4]. The input can represent a malicious
attack, aiming at disrupting the normal system functioning while staying undetected [5]. The best security in CPSs can be
obtained by combining classical tools for cyber-security with control-theoretic tools taking into account the physical
system, for example, by exploiting the known dynamics of the system to detect inconsistencies in the measurements due
to the attack (the undesired input to be detected).
Current research on observability and input detection can be enhanced by taking into account sparsity constraints. Sparsity
often appears at many levels in CPSs, due to physical limitations in the range of interconnections between subsystems and
of communications. Moreover, in the study of security, it is often natural to assume some sparsity of the attacker input,
modelling the difficulty to physically disrupt the system at too many locations [6]. The question of observability or
input detectability under given sparsity constraints could lead to different answers than the ones obtained in classical
control theory, similarly to the compressive-sensing revolutionary approach to signal processing, where non-uniqueness of
reconstruction due to low sampling rate can be overcome by taking sparsity into account in the problem formulation [7].
Bibliography
[1] Y. Y. Liu, J. J. Slotine, and A. L. Barabasi, Controllability of complex networks, Nature, vol. 473, no. 7346, pp. 167–173,
2011
[2] A. Y. Kibangou and C. Commault, Observability in connected strongly regular graphs and distance regular graphs, IEEE
Trans. Control of Networks, (1) 4, pp. 360-369, 2014.
[3] F. Garin and Y. Yuan, Distributed privacy-preserving network size computation: A system-identification based method, 52nd
IEEE Conf. on Decision and Control (CDC 2013), pp. 5438-5443,. Dec. 2013.
[4] A. Esna Ashari, F. Garin, and A.Y. Kibangou, Joint Input and State Estimation for Linear Discrete-Time Networked Systems,
IFAC Workshop Necsys'12, pp. 97-102, Sept. 2012.
[5] F. Pasqualetti, F. Dörfler, and F. Bullo, Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for
Optimal Cross-Layer Resilient Control Systems. IEEE Control Systems Magazine, 35(1):110-127, 2015.
[6] A. Teixeira, I. Shames, H. Sandberg, and K. H. Johansson, A Secure Control Framework for Resource-Limited Adversaries,
Automatica, 51, pp. 135-148, 2015
[7] D. L. Donoho, Compressed sensing, IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289–1306, 2006.