Statistical structure learning of smart grid for detection of false data injection

Sedghi, Hanie; Jonckheere, Edmond

doi:10.1109/pesmg.2013.6672176

Cited by 25 publications

(18 citation statements)

References 10 publications

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“…In fact, they are stronger than Theorem 1 since we did not find any case where the LD detector results in C * (LD) such that C * (LD) = 0 and supp C * (LD) ⊆ I. (To Side) 1, 3,4,8,9,12,13,14,15,18,19,21,22,27,31,32,35,36,45,47,48,50,51,56,61,65,66,67,68,69,73,78,79,91,92,94,111,112,113,115,116,117,118,119,120,123,124,125,127,131,132,134,140,145,146,160,166,168,174,…”

Section: Numerical Resultsmentioning

confidence: 82%

False data injection attacks on phasor measurements that bypass low-rank decomposition

Zhang

Chu

Sankar

et al. 2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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This paper studies the vulnerability of phasor measurement units (PMUs) to false data injection (FDI) attacks. Prior work demonstrated that unobservable FDI attacks that can bypass traditional bad data detectors based on measurement residuals can be identified by detector based on low-rank decomposition (LD). In this work, a class of more sophisticated FDI attacks that captures the temporal correlation of PMU data is introduced. Such attacks are designed with a convex optimization problem and can always bypass the LD detector. The vulnerability of this attack model is illustrated on both the IEEE 24-bus RTS and the IEEE 118-bus systems.

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Section: Numerical Resultsmentioning

confidence: 82%

False data injection attacks on phasor measurements that bypass low-rank decomposition

Zhang

Chu

Sankar

et al. 2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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“…In the smart-world systems (e.g., smart cities, smart grid, smart transportation), a number of efforts have been devoted to studying the impacts of cyber attacks as well as the development of defensive schemes [5,6,7,11,13,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32]. For example, Ericsson et al [33] presented some important issues on the cyber security and information security in the energy-based cyber-physical systems.…”

Section: Related Workmentioning

confidence: 99%

Towards an Iterated Game Model with Multiple Adversaries in Smart-World Systems

Yang

et al. 2018

Sensors

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Diverse and varied cyber-attacks challenge the operation of the smart-world system that is supported by Internet-of-Things (IoT) (smart cities, smart grid, smart transportation, etc.) and must be carefully and thoughtfully addressed before widespread adoption of the smart-world system can be fully realized. Although a number of research efforts have been devoted to defending against these threats, a majority of existing schemes focus on the development of a specific defensive strategy to deal with specific, often singular threats. In this paper, we address the issue of coalitional attacks, which can be launched by multiple adversaries cooperatively against the smart-world system such as smart cities. Particularly, we propose a game-theory based model to capture the interaction among multiple adversaries, and quantify the capacity of the defender based on the extended Iterated Public Goods Game (IPGG) model. In the formalized game model, in each round of the attack, a participant can either cooperate by participating in the coalitional attack, or defect by standing aside. In our work, we consider the generic defensive strategy that has a probability to detect the coalitional attack. When the coalitional attack is detected, all participating adversaries are penalized. The expected payoff of each participant is derived through the equalizer strategy that provides participants with competitive benefits. The multiple adversaries with the collusive strategy are also considered. Via a combination of theoretical analysis and experimentation, our results show that no matter which strategies the adversaries choose (random strategy, win-stay-lose-shift strategy, or even the adaptive equalizer strategy), our formalized game model is capable of enabling the defender to greatly reduce the maximum value of the expected average payoff to the adversaries via provisioning sufficient defensive resources, which is reflected by setting a proper penalty factor against the adversaries. In addition, we extend our game model and analyze the extortion strategy, which can enable one participant to obtain more payoff by extorting his/her opponents. The evaluation results show that the defender can combat this strategy by encouraging competition among the adversaries, and significantly suppress the total payoff of the adversaries via setting the proper penalty factor.

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“…Existing approaches on state estimation in the presence of data substitutions [3], [17], [25], [28], [36], [38] usually attempt to identify and protect some key PMUs so as to prevent the intruders from injecting unobservable attacks. To the best of our knowledge, only one recent paper [38] addresses the detection of unobservable attacks in Supervisory Control and Data Acquisition (SCADA) system.…”

Section: Data Substitutionmentioning

confidence: 99%

“…To the best of our knowledge, only one recent paper [38] addresses the detection of unobservable attacks in Supervisory Control and Data Acquisition (SCADA) system. The detection method proposed in [38] is based on statistical learning and relies on the assumption that the measurements at different time instants are independent and identically distributed samples of random variables. This assumption might not hold when the system is experiencing some disturbances.…”

Section: Data Substitutionmentioning

confidence: 99%

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data

Wang

Chow

Gao

et al. 2015

2015 48th Hawaii International Conference on System Sciences

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With the installation of many new multi-channel phasor measurement units (PMUs), utilities and power grid operators are collecting an unprecedented amount of highsampling rate bus frequency, bus voltage phasor, and line current phasor data with accurate time stamps. The data owners are interested in efficient algorithms to process and extract as much information as possible from such data for real-time and off-line analysis. Traditional data analysis typically analyze one channel of PMU data at a time, and then combine the results from the individual analysis to arrive at some conclusions. In this paper, a spatial-temporal framework for efficient processing of blocks of PMU data is proposed. A key property of these PMU data matrices is that they are low rank. Using this property, various data management issues such as data compression, missing data recovery, data substitution detection, and disturbance triggering and location can be processing using singular-value based algorithms and convex programming. These functions are illustrated using some historical data from the Central New York power system.

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Statistical structure learning of smart grid for detection of false data injection

Cited by 25 publications

References 10 publications

False data injection attacks on phasor measurements that bypass low-rank decomposition

False data injection attacks on phasor measurements that bypass low-rank decomposition

Towards an Iterated Game Model with Multiple Adversaries in Smart-World Systems

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data

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