The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial control processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (ii) we design centralized and distributed attack detection and identification monitors. Finally, we validate our findings through compelling examples.

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Attack Detection and Identification in Cyber-Physical Systems

Robust adversarial learning is considered in the context of closed-loop control with adversarial signaling in this paper. Due to the nature of incomplete information of the control agent about the environment, the belief-dependent signaling game formulation is introduced in the dynamic system and a dynamic cheap talk game is formulated with belief-dependent strategies for both players. We show that the dynamic cheap talk game can further be reformulated as a particular stochastic game, where the states are beliefs of the environment and the actions are the adversarial manipulation strategies and control strategies. Furthermore, the bisimulation metric is proposed and studied for the dynamic cheap talk game, which provides an upper bound on the difference between values of different initial beliefs in the zero-sum equilibrium.

Dynamic Cheap Talk for Robust Adversarial Learning

A large number of peer-to-peer streaming systems has been proposed and deployed in recent years. Yet, there is no clear understanding of how these systems scale and how multi-path and multihop transmission, properties of all recent systems, affect the quality experienced by the peers. In this paper we present an analytical study that considers the relationship between delay and loss for general overlays: we study the trade-off between the playback delay and the probability of missing a packet and we derive bounds on the scalability of the systems. We use an exact model of push-based overlays to show that the bounds hold under diverse conditions: in the presence of errors, under node churn, and when using forward error correction and various retransmission schemes.

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Delay bounds and scalability for overlay multicast

Real-time monitoring and control in power systems is increasingly dependent on Phasor Measurement Units (PMUs). PMUs depend on precise time synchronization, and thus it is essential to ensure the security of time synchronization. In this paper we consider the detection of low-rate time synchronization attacks against PMUs. Based on a general clock model and a PMU measurement model we provide a closed form expression for the correlation between the clock frequency adjustments and the measured PMU phase angles in the absence of an attack. Leveraging the intuition that an attack would affect the correlation between these two quantities, we propose a model-based and a non-parametric correlation-based detector for time synchronization attacks. We evaluate the proposed detectors using extensive simulations. Our results show that they outperform traditional change detection techniques for clocks with low accuracy, for which attack detection is most challenging.

Correlation-based Detection of PMU Time Synchronization Attacks

Phasor measurement units (PMU) rely on an accurate time-synchronization to phase-align the phasors and timestamp the voltage and current phasor measurements. Among the symmetrical components computed from the phasors in three-phase systems, the standard practice only uses the direct-sequence component for state estimation and bad data detection (BDD). Time-synchronization attacks (TSAs) can compromise the measured phasors and can, thus, significantly alter the state estimate in a manner that is undetectable by widely used power-system BDD algorithms. In this paper we investigate the potential of utilizing the three-phase model instead of the direct-sequence model for mitigating the vulnerability of state estimation to undetectable TSAs. We show analytically that if the power system is unbalanced then the use of the three-phase model as input to BDD algorithms enables to detect attacks that would be undetectable if only the direct-sequence model was used. Simulations performed on the IEEE 39-bus benchmark using real load profiles recorded on the grid of the city of Lausanne confirm our analytical results. Our results provide a new argument for the adoption of three-phase models for BDD, as their use is a simple, yet effective measure for reducing the vulnerability of PMU measurements to TSAs.

Time-Synchronization Attack Detection in Unbalanced Three-Phase Systems

Peer-to-peer (P2P) systems are responsible for a large fraction of inter-ISP transit traffic in the Internet. Hence, many Internet service providers (ISPs) have deployed P2P caches to decrease their P2P related transit traffic. In our work we consider the problem of allocating the limited upload capacity of a P2P cache between a set of overlay swarms. The goal of cache capacity allocation is to increase the amount of transit traffic that can be saved using the cache. Our preliminary results are based on analytical models and simulations and show that cache capacity allocation is a promising means of improving the efficiency of P2P caches. We are currently validating our results via experiments on Planet-Lab.

Gyorgy Dan

Papers

Dynamic Cheap Talk for Robust Adversarial Learning

Delay bounds and scalability for overlay multicast

Correlation-based Detection of PMU Time Synchronization Attacks

Time-Synchronization Attack Detection in Unbalanced Three-Phase Systems

On the benefits of P2P cache capacity allocation