Smart Grid Data Integrity Attacks

Giani, Annarita; Bitar, Eilyan; García, María Teresa Iglesias; McQueen, Miles; Khargonekar, Pramod P.; Poolla, Kameshwar

doi:10.1109/tsg.2013.2245155

Cited by 201 publications

(138 citation statements)

References 27 publications

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“…As shown in Table 4, the IEEE 14-bus system is divided into two subsystems, "subsys_1" and "subsys_2". L 5,6 , L 4,7 , L 4,9 are tie lines. In subsys_1, there are 8 buses including adjacent buses: bus 6 , bus 7 and bus 9 .…”

Section: Attack Cases On Ieee 14-bus Systemsmentioning

confidence: 99%

“…L 5,6 , L 4,7 , L 4,9 are tie lines. In subsys_1, there are 8 buses including adjacent buses: bus 6 , bus 7 and bus 9 . The number of state variables n 1 is 15 and the number of measurements m 1 is 40.…”

Section: Attack Cases On Ieee 14-bus Systemsmentioning

confidence: 99%

“…In Attack Case 2, false data is only injected into subsys_2. The P 6,13 is modified from 17.75 MW to 53.24 MW, and P 13,6 is modified from −17.54 MW to 52.61 MW. In Attack Case 3, the false data is injected into the tie line between subsys_1 and subsys_2.…”

Section: Attack Cases On Ieee 14-bus Systemsmentioning

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%

“…Phasor measurement units (PMUs) have become increasingly deployed in power systems, providing accurate, synchronous, and secure sampling of the system states. How to economically deploy PMUs to best facilitate the state estimator to detect FDI attacks has become an interesting problem [6,14]. In addition, many smart algorithms are applied to detect the false data, such as geometrically-designed residual filter [15], and the adaptive cumulative sum (CUSUM) test [16].…”

Section: Introductionmentioning

confidence: 99%

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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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