Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Drayer, Elisabeth; Routtenberg, Tirza

doi:10.1109/jsyst.2019.2927469

Cited by 111 publications

(73 citation statements)

References 55 publications

(129 reference statements)

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“…Damaging or vandalize components of smart grids [7], [40], [82], [94], [122], [144], [150] Causing service disturbance and potentially blackouts…”

Section: Physical Threatsmentioning

confidence: 99%

“…The DoS attacks often employ the IP spoofing to overload networks along with devices with packets, which seem to be from legitimate source IP addresses. The FDI attacks are caused by injecting auxiliary signals or altering the measurement content conveyed via the sensors [150]. Meanwhile, the MITM attack occurs in the communication links [151].…”

Section: Analysis and Impact Of Cyber-attacks Vulnerability On mentioning

confidence: 99%

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CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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“…Damaging or vandalize components of smart grids [7], [40], [82], [94], [122], [144], [150] Causing service disturbance and potentially blackouts…”

Section: Physical Threatsmentioning

confidence: 99%

Section: Analysis and Impact Of Cyber-attacks Vulnerability On mentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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“…There can be an infinite number of ways to design physical cyber attacks for a system with LQG control. Since LFA affects a control inputs and NIA affects an observation as their direct effects can be seen in (12) and (14), the linear forward attack is designed to degrade LQR performance while the NIA is designed to degrade the performance of state estimation.…”

Section: Linear Forward Attack and Defensementioning

confidence: 99%

“…A robust sliding mode control was developed for a class of Markovian jump system to deal with FDIA into control signals and peak-bounded external disturbances [13]. To detect the presence of CPS attacks, transformations such as the graph Fourier transform [14] and cross wavelet transform [15] were exploited A control system was transformed into a switched auxiliary system to develop a covert attack detection system using a switched Luenberger observer [16].…”

Section: Introductionmentioning

confidence: 99%

A Controllable False Data Injection Attack for a Cyber Physical System

Yang

2021

IEEE Access

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“…it is only a function of the pdf of the first-stage observations. The PSMSE in (9), which is the MSE over the selected parameter, is widely used in the mathematical statistics literature and in practical experiment design [21]- [27].…”

Section: Two-stage Psmse and ψ-Unbiasednessmentioning

confidence: 99%

Low-Complexity Methods for Estimation After Parameter Selection

Harel

Routtenberg

2020

IEEE Trans. Signal Process.

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Statistical inference of multiple parameters often involves a preliminary parameter selection stage. The selection stage has an impact on subsequent estimation, for example by introducing a selection bias. The post-selection maximum likelihood (PSML) estimator is shown to reduce the selection bias and the post-selection mean-squared-error (PSMSE) compared with conventional estimators, such as the maximum likelihood (ML) estimator. However, the computational complexity of the PSML is usually high due to the multi-dimensional exhaustive search for a global maximum of the post-selection log-likelihood (PSLL) function. Moreover, the PSLL involves the probability of selection that, in general, does not have an analytical form. In this paper, we develop new low-complexity post-selection estimation methods for a two-stage estimation after parameter selection architecture. The methods are based on implementing the iterative maximization by parts (MBP) approach, which is based on the decomposition of the PSLL function into "easilyoptimized" and complicated parts.The proposed second-best PSML method applies the MBP-PSML algorithm with a pairwise probability of selection between the two highest-ranked parameters w.r.t. the selection rule. The proposed SA-PSML method is based on using stochastic approximation (SA) and Monte Carlo integrations to obtain a non-parametric estimation of the gradient of the probability of selection and then applying the MBP-PSML algorithm on this approximation. For low-complexity performance analysis, we develop the empirical post-selection Cramér-Rao-type lower bound. Simulations demonstrate that the proposed post-selection estimation methods are tractable and reduce both the bias and the PSMSE, compared with the ML estimator, while only requiring moderate computational complexity.

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Detection of False Data Injection Attacks in Smart Grids Based on Graph Signal Processing

Abstract: 0.2 −0.1 0 τ J J normalized frequency frequency response FJ{ ψ J } FJ{ ψ J }FDI 1 Fig. 10. Test case 4: Frequency response of F J { ψ J } with and without undetectable FDI attack. For the attacked case, F J { ψ J } FDI , the threshold τ J J is exceeded.

Cited by 111 publications

References 55 publications

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

A Controllable False Data Injection Attack for a Cyber Physical System

Low-Complexity Methods for Estimation After Parameter Selection

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