The interaction between information technology and physical world makes Cyber-Physical Systems (CPS) vulnerable to malicious attacks beyond the standard cyber attacks. This has motivated the need for attack-resilient state estimation. Yet, the existing state-estimators are based on the non-realistic assumption that the exact system model is known. Consequently, in this work we present a method for state estimation in presence of attacks, for systems with noise and modeling errors. When the the estimated states are used by a state-based feedback controller, we show that the attacker cannot destabilize the system by exploiting the difference between the model used for the state estimation and the real physical dynamics of the system. Furthermore, we describe how implementation issues such as jitter, latency and synchronization errors can be mapped into parameters of the state estimation procedure that describe modeling errors, and provide a bound on the state-estimation error caused by modeling errors. This enables mapping control performance requirements into real-time (i.e., timing related) specifications imposed on * This material is based on research sponsored by DARPA under agreement number FA8750-12-2-0247. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.

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Abstract-We present a method to stabilize a plant with a network of resource constrained wireless nodes. As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller (perhaps performing some encoding along the way), our approach treats the network itself as the controller. Specifically, we formulate a strategy for each node in the network to follow, where at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. We show that this causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. We provide a numerical design procedure to determine appropriate linear combinations to be applied by each node so that the transmissions of the nodes closest to the actuators will stabilize the plant. We also show how our design procedure can be modified to maintain mean square stability under packet drops in the network, and present a distributed scheme that can handle node failures while preserving stability. We call this architecture a Wireless Control Network, and show that it introduces very low computational and communication overhead to the nodes in the network, allows the use of simple transmission scheduling algorithms, and enables compositional design (where the existing wireless control infrastructure can be easily extended to handle new plants that are brought online in the vicinity of the network).

Abstract. The design and implementation of software for medical devices is challenging due to their rapidly increasing functionality and the tight coupling of computation, control, and communication. The safetycritical nature and the lack of existing industry standards for verification, make this an ideal domain for exploring applications of formal modeling and analysis. In this study, we use a dual chamber implantable pacemaker as a case study for modeling and verification of control algorithms for medical devices in UPPAAL. We begin with detailed models of the pacemaker, based on the specifications and algorithm descriptions from Boston Scientific. We then define the state space of the closed-loop system based on its heart rate and developed a heart model which can nondeterministically cover the whole state space. For verification, we first specify unsafe regions within the state space and verify the closed-loop system against corresponding safety requirements. As stronger assertions are attempted, the closed-loop unsafe state may result from healthy openloop heart conditions. Such unsafe transitions are investigated with two clinical cases of Pacemaker Mediated Tachycardia and their corresponding correction algorithms in the pacemaker. Along with emerging tools for code generation from UPPAAL models, this effort enables modeldriven design and certification of software for medical devices.