Bad data injection in smart grid: attack and defense mechanisms

Huang, Yi; Esmalifalak, Mohammad; Nguyen, Huy; Zheng, Rong; Han, Zhu; Li, Husheng; Song, Lingyang

doi:10.1109/mcom.2013.6400435

Cited by 145 publications

(78 citation statements)

References 11 publications

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“…According to [7], a computationally efficient algorithm was derived to detect false data injection attacks using the generalized likelihood ratio test (GLRT) in the Bayesian framework. Huang et al [8] and [9] proposed cumulative sum (CUSUM)-based quickest detection (QD) to study the tradeoff between the attack detection speed and performance.…”

Section: Related Workmentioning

confidence: 99%

Protection Strategies Against False Data Injection Attacks with Uncertain Information on Electric Power Grids

Bae¹,

Lee²,

Kim³

et al. 2017

Journal of Electrical Engineering and Technology

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-False data injection attacks have recently been introduced as one of important issues related to cyber-attacks on electric power grids. These attacks aim to compromise the readings of multiple power meters in order to mislead the operation and control centers. Recent studies have shown that if a malicious attacker has complete knowledge of the power grid topology and branch admittances, s/he can adjust the false data injection attack such that the attack remains undetected and successfully passes the bad data detection tests that are used in power system state estimation. In this paper, we investigate that a practical false data injection attack is essentially a cyber-attack with uncertain information due to the attackers lack of knowledge with respect to the power grid parameters because the attacker has limited physical access to electric facilities and limited resources to compromise meters. We mathematically formulated a method of identifying the most vulnerable locations to false data injection attack. Furthermore, we suggest minimum topology changes or phasor measurement units (PMUs) installation in the given power grids for mitigating such attacks and indicate a new security metrics that can compare different power grid topologies. The proposed metrics for performance is verified in standard IEEE 30-bus system. We show that the robustness of grids can be improved dramatically with minimum topology changes and low cost.

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Section: Related Workmentioning

confidence: 99%

Protection Strategies Against False Data Injection Attacks with Uncertain Information on Electric Power Grids

Bae¹,

Lee²,

Kim³

et al. 2017

Journal of Electrical Engineering and Technology

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“…(F/100)−1 (9) After obtaining the model orders and parameters, the identified models can be evaluated by comparing their outputs with the actual system responses. The objective function for the model evaluation is e (F/100)−1 .…”

Section: Model Structure For Dynamic Response Estimationmentioning

confidence: 99%

Measurement‐based correlation approach for power system dynamic response estimation

Bai

Liu

et al. 2015

IET gener. transm. distrib.

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Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system-synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement-based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto-regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy.

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“…Then the manipulated measurement z a = z + a can pass the bad data detection and identification of direct-current (DC) SE [8,9]. Thus, the measurement residual is:…”

Section: Tfdimentioning

confidence: 99%

“…While relatively effective against random noises, these detectors lack the ability to detect specialized and highly structured false data that conforms to the network topology and some particular physical laws. Recent works [4][5][6][7][8][9][10] have demonstrated that an adversary, armed with knowledge about the network's configuration, can successfully construct undetectable FDI attacks on SE avoiding detection. However, there are still some obstacles to launch such attacks.…”

Section: Introductionmentioning

confidence: 99%

Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids

et al. 2014

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False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector's tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118-and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs.

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Bad data injection in smart grid: attack and defense mechanisms

Cited by 145 publications

References 11 publications

Protection Strategies Against False Data Injection Attacks with Uncertain Information on Electric Power Grids

Protection Strategies Against False Data Injection Attacks with Uncertain Information on Electric Power Grids

Measurement‐based correlation approach for power system dynamic response estimation

Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids

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