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False Data Injection Attacks against State Estimation in Electric Power Grids
, 2009
"... 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 ..."
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Cited by 151 (2 self)
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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, nonrandom 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.
Improved MFOCUSS algorithm with overlapping blocks for locally smooth sparse signals
 IEEE Trans. Signal Process
"... CUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized MFOCUSS algorithm has been recently proposed by Cotter et al. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this ..."
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Cited by 8 (0 self)
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CUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized MFOCUSS algorithm has been recently proposed by Cotter et al. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this paper, we propose three modifications to the MFOCUSS algorithm to make it more efficient for sparse and locally smooth solutions. First, motivated by the simultaneously autoregressive (SAR) model, we incorporate an additional weighting (smoothing) matrix into the Tikhonov regularization term. Next, the entire set of measurement vectors is divided into blocks, and the solution is updated sequentially, based on the overlapping of data blocks. The last modification is based on an alternating minimization technique to provide datadriven (simultaneous) estimation of the regularization parameter with the generalized crossvalidation (GCV) approach. Finally, the simulation results demonstrating the benefits of the proposed modifications support the analysis. Index Terms—FOCal Underdetermined System Solver (FOCUSS), generalized crossvalidation (GCV), smooth signals, sparse solutions, underdetermined systems. I.
BasisDetect: A Modelbased Network Event Detection Framework
"... The ability to detect unexpected events in large networks can be a significant benefit to daily network operations. A great deal of work has been done over the past decade to develop effective anomaly detection tools, but they remain virtually unused in live network operations due to an unacceptably ..."
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Cited by 2 (2 self)
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The ability to detect unexpected events in large networks can be a significant benefit to daily network operations. A great deal of work has been done over the past decade to develop effective anomaly detection tools, but they remain virtually unused in live network operations due to an unacceptably high false alarm rate. In this paper, we seek to improve the ability to accurately detect unexpected network events through the use of BasisDetect, a flexible but precise modeling framework. Using a small dataset with labeled anomalies, the BasisDetect framework allows us to define large classes of anomalies and detect them in different types of network data, both from single sources and from multiple, potentially diverse sources. Network anomaly signal characteristics are learned via a novel basis pursuit based methodology. We demonstrate the feasibility of our BasisDetect framework method and compare it to previous detection methods using a combination of synthetic and realworld data. In comparison with previous anomaly detection methods, our BasisDetect methodology results show a 50 % reduction in the number of false alarms in a single node dataset, and over 65 % reduction in false alarms for synthetic networkwide data.
Greedy Methods in Plume Detection, Localization and Tracking
 Advances in Greedy Algorithms , edited by W. Bednorz, InTech
, 2008
"... Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then so ..."
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Cited by 2 (0 self)
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Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively makes
SPEECH DENOISING BASED ON A GREEDY ADAPTIVE DICTIONARY ALGORITHM
"... In this paper we consider the problem of speech denoising based on a greedy adaptive dictionary (GAD) algorithm. The transform is orthogonal by construction, and is found to give a sparse representation of the data being analysed, and to be robust to additive Gaussian noise. The performance of the a ..."
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In this paper we consider the problem of speech denoising based on a greedy adaptive dictionary (GAD) algorithm. The transform is orthogonal by construction, and is found to give a sparse representation of the data being analysed, and to be robust to additive Gaussian noise. The performance of the algorithm is compared to that of the principal component analysis (PCA) method, for a speech denoising application. It is found that the GAD algorithm offers a sparser solution than PCA, while having a similar performance in the presence of noise. 1.
Master’s Examination Committee: Approved by
"... Channel Estimation is an essential component in applications such as radar and data communication. In multi path time varying environments, it is necessary to estimate timeshifts, scaleshifts (the wideband equivalent of Dopplershifts), and the gains/phases of each of the multiple paths. With rece ..."
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Channel Estimation is an essential component in applications such as radar and data communication. In multi path time varying environments, it is necessary to estimate timeshifts, scaleshifts (the wideband equivalent of Dopplershifts), and the gains/phases of each of the multiple paths. With recent advances in sparse estimation (or “compressive sensing”), new estimation techniques have emerged which yield more accurate estimates of these channel parameters than traditional strategies. These estimation strategies, however, restrict potential estimates of timeshifts and scaleshifts to a finite set of values separated by a choice of grid spacing. A small grid spacing increases the number of potential estimates, thus lowering the quantization error, but also increases complexity and estimation time. Conversely, a large grid spacing lowers the number of potential estimates, thus lowering the complexity and estimation time, but increases the quantization error. In this thesis, we derive
1 Improved MFOCUSS Algorithm with Overlapping Blocks for Locally Smooth Sparse Signals
"... CUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized MFOCUSS algorithm has been recently proposed by Cotter et al. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this ..."
Abstract
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CUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized MFOCUSS algorithm has been recently proposed by Cotter et al. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this paper, we propose three modifications to the MFOCUSS algorithm in order to make it more efficient for sparse and locally smooth solutions. First, motivated by the Simultaneously Autoregressive (SAR) model, we incorporate an additional weighting (smoothing) matrix into the Tikhonov regularization term. Next, the entire set of measurement vectors is divided into blocks, and the solution is updated sequentially, based on the overlapping of data blocks. The last modification is based on an alternating minimization technique to provide datadriven (simultaneous) estimation of the regularization parameter with the Generalized CrossValidation (GCV) approach. Finally, the simulation results demonstrating the benefits of the proposed modifications support the analysis. Index Terms — FOCUSS, underdetermined systems, sparse solutions, smooth signals, GCV
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 ..."
Abstract
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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
Ways to Sparse Representation: A Comparative Study*
"... Abstract: Many algorithms have been proposed to achieve sparse representation over redundant dictionaries or transforms. A comprehensive understanding of these algorithms is needed when choosing and designing algorithms for particular applications. This research studies a representative algorithm fo ..."
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Abstract: Many algorithms have been proposed to achieve sparse representation over redundant dictionaries or transforms. A comprehensive understanding of these algorithms is needed when choosing and designing algorithms for particular applications. This research studies a representative algorithm for each category, matching pursuit (MP), basis pursuit (BP), and noise shaping (NS), in terms of their sparsifying capability and computational complexity. Experiments show that NS has the best performance in terms of sparsifying capability with the least computational complexity. BP has good sparsifying capability, but is computationally expensive. MP has relatively poor sparsifying capability and the computations are heavily dependent on the problem scale and signal complexity. Their performance differences are also evaluated for three typical applications of timefrequency analyses, signal denoising, and image coding. NS has good performance for timefrequency analyses and image coding with far fewer computations. However, NS does not perform well for signal denoising. This study provides guidelines for choosing an algorithm for a given problem and for designing or improving algorithms for sparse representation. Key words: sparse representation; redundant dictionary/transform; nonlinear approximation; matching pursuit; basis pursuit; noise shaping
TITLE: SENSING DICTIONARY CONSTRUCTION FOR OR THOGONALMATCHING PURSUIT ALGORITHM IN COMPRESSIVE SENSING
"... ii To my supervisor, my parents and my grandmother In compressive sensing, the fundamental problem is to reconstruct sparse signal from its nonadaptive insufficient linear measurement. Besides sparse signal reconstruction algorithms, measurement matrix or measurement dictionary plays an important pa ..."
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ii To my supervisor, my parents and my grandmother In compressive sensing, the fundamental problem is to reconstruct sparse signal from its nonadaptive insufficient linear measurement. Besides sparse signal reconstruction algorithms, measurement matrix or measurement dictionary plays an important part in sparse signal recovery. Orthogonal Matching Pursuit (OMP) algorithm, which is widely used in compressive sensing, is especially affected by measurement dictionary. Measurement dictionary with small restricted isometry constant or coherence could improve the performance of OMP algorithm. Based on measurement dictionary, sensing dictionary can be constructed and can be incorporated into OMP algorithm. In this thesis, two methods are proposed to design sensing dictionary. In the first method, sensing dictionary design problem is formulated as a linear programming problem. The solution is unique and can be obtained by standard linear programming method such as primaldual interior point method. The major drawback of