In this paper, we characterize and analyze the set of strategic stealthy false-data injection attacks on discrete-time linear systems. In particular, the threat scenarios tackled in the paper consider adversaries that aim at deteriorating the system's performance by maximizing the corresponding quadratic cost function, while remaining stealthy with respect to anomaly detectors. As opposed to other work in the literature, the effect of the adversary's actions on the anomaly detector's output is not constrained to be zero at all times. Moreover, scenarios where the adversary has uncertain model knowledge are also addressed. The set of strategic attack policies is formulated as a non-convex constrained optimization problem, leading to a sensitivity metric denoted as the output-to-output ℓ2-gain. Using the framework of dissipative systems, the output-to-output gain is computed through an equivalent convex optimization problem. Additionally, we derive necessary and sufficient conditions for the output-to-output gain to be unbounded, with and without model uncertainties, which are tightly related to the invariant zeros of the system.

Cyber-secure networked control is modeled, analyzed, and experimentally illustrated in this paper. An attack space dened bythe adversary's model knowledge, disclosure, and disruption resources is introduced. Adversaries constrained by these resourcesare modeled for a networked control system architecture. It is shown that attack scenarios corresponding to denial-of-service,replay, zero-dynamics, and bias injection attacks on linear time-invariant systems can be analyzed using this framework.Furthermore, the attack policy for each scenario is described and the attack's impact is characterized using the concept ofsafe sets. An experimental setup based on a quadruple-tank process controlled over a wireless network is used to illustrate theattack scenarios, their consequences, and potential counter-measures.

In this chapter, we survey cyber security solutions for control and monitoring systems that are used to manage the Smart Grid. We start with a short review of the history and use of Industrial Control Systems (ICSs) and Supervisory Control and Data Acquisition (SCADA) systems, and how cyber security in control systems has recently become a major concern, in the wake of the Stuxnet and other recently discovered malware. We follow up with surveying information technology and control-centric security tools that can be used to improve the resilience of industrial control systems. Feedback control loops are core components in the Smart Grid, as they enable the maximal utilization of the physical infrastructure and its resources. As the number of control loops in the Smart Grid increases, the cyber security challenges faced by ICSs become increasingly important within the Smart Grid's context. To highlight such novel challenges, we give an overview of the envisioned control loops in future Smart Grids, and discuss the potential impact of cyber threats targeting critical Smart Grid functionalities. As a case study, false-data injection attacks on power transmission networks are considered. The level of resilience to such attacks is assessed through a control-centric risk assessment methodology, which is also used for allocating the deployment of more modern and secure equipment. The chapter ends with a discussion of future research challenges in the area.

The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the impact of the algorithm parameters on the convergence times of the method is still lacking. In this paper we find the optimal algorithm parameters that minimize the convergence factor of the ADMM iterates in the context of l2-regularized minimization and constrained quadratic programming. Numerical examples show that our parameter selection rules significantly outperform existing alternatives in the literature.

Critical infrastructures must continuously operate safely and reliably, despite a variety of potential system disturbances. Given their strict operating requirements, such systems are automated and controlled in real time by several digital controllers receiving measurements from sensors and transmitting control signals to actuators. Since these physical systems are often spatially distributed, there is a need for information technology (IT) infrastructures enabling the timely data flow between the system components. These networked control systems are ubiquitous in modern societies [1]. Examples include the electric power network, intelligent transport systems, and industrial processes.

In this paper, we study the impact of adversarial actions on voltage control schemes in interconnected microgrids. Each microgrid is abstracted as a power inverter that can be controlled to regulate its voltage magnitude and phase-angle independently. Moreover, each power inverter is modeled as a single integrator, whose input is given by a voltage droop-control policy that is computed based on voltage magnitude and reactive power injection measurements. Under mild assumptions, we then establish important properties of the nominal linearized closed-loop system, such as stability, positivity, and diagonal dominance. These properties play an important role when characterizing the potential impact of different attack scenarios. In particular, we discuss two attack scenarios where the adversary corrupts measurement data and reference signals received by the voltage droop controllers. The potential impact of instances of each scenario is analyzed using control-theoretic tools, which may be used to develop methodologies for identifying high-risk attack scenarios, as is illustrated by numerical examples.

We propose and evaluate a down-sampled controller which reduces the network usage while providing a guaranteed desired linear quadratic control performance. This method is based on fast and slow sampling intervals, as the closed-system benefits by being brought quickly to steady-state conditions while behaving satisfactorily when being actuated at a slow rate once at those conditions. This mechanism is shown to provide large savings with respect to network usage when compared to traditional periodic time-triggered control and other aperiodic controllers proposed in the literature.

This work presents a distributed framework for coordination of flexible electricity consumption for a number of households in the distribution grid. Coordination is conducted with the purpose of minimizing a trade-off between individual concerns about discomfort and electricity cost, on the one hand, and joint concerns about grid losses and voltage variations on the other. Our contribution is to demonstrate how distributed coordination of both active and reactive consumption may be conducted, when consumers are jointly coupled by grid losses and voltage variations. We further illustrate the benefit of including consumption coordination for grid operation, and how different types of consumption present different benefits.

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark.

We consider a Gaussian cheap talk game with quadratic cost functions. The cost function of the receiver is equal to the estimation error variance, however, the cost function of each senders contains an extra term which is captured by its private information. Following the cheap talk literature, we model this problem as a game with asymmetric information. We start by the single sender case in which the receiver also has access to a noisy but honest side information in addition to the message transmitted by a strategic sender. We generalize this setup to multiple sender case. For the multiple sender case, we observe that if the senders are not herding (i. e., copying each other policies), the quality of the receiver's estimation degrades rapidly as the number of senders increases.