Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid

Lakshminarayana, Subhash; Wen, Fuxi; Yau, David K. Y.

doi:10.1109/icassp.2018.8461493

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Since these topology attacks depend on historical measurements, the measurement matrix H cannot be learned by the attacker immediately. The learning/inferring process usually takes a sufficiently long time (hours or days) due to the exfiltration of an enough amount of historical measurement data [27,[38][39][40][41]. • The attacker is able to eavesdrop and tamper with the measurements by intruding into the communication network or IP-accessible field devices [42].…”

Section: B Threat Modelmentioning

confidence: 99%

“…In fact, the attacker might try to learn the perturbed measurement matrix H ′ . But the learning process usually takes a sufficiently long time (hours or days) due to the exfiltration of an enough amount of historical measurement data [27,[38][39][40][41]. In other words, the attacker cannot obtain the latest measurement matrix immediately.…”

Section: Problem Statementmentioning

confidence: 99%

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Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Zhang

Deng

Yau

et al. 2020

IEEE Trans.Inform.Forensic Secur.

109

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Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l ≥ 2n, where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

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Section: B Threat Modelmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Zhang

Deng

Yau

et al. 2020

IEEE Trans.Inform.Forensic Secur.

109

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“…In (2), the attacker can obtain the knowledge of power flows F lm,p by monitoring the line flow sensor measurements. On the other hand, the line reactances x lm can be learned by monitoring the grid power flows over a period of time using existing techniques [19], [20]. The attacker can also learn the reactance of the disconnected branch x l similarly.…”

Section: Power Grid Modelmentioning

confidence: 99%

“…Hence, an attack constructed using outdated knowledge of the system can be detected by the BDD. (The reader can refer to [20] for practical guidance on how frequently the branch reactances must be perturbed.) In this section, we first formalize the MTD design problem to defend against CCPAs.…”

Section: Moving-target Defense For Ccpasmentioning

confidence: 99%

Moving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids

Lakshminarayana

Belmega

Poor

2019

2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid's transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost.

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“…H. Vincent Poor is with the Department of Electrical Engineering, Princeton University, USA (email: poor@princeton.edu). The work was partially presented at ICASSP-2018 [1].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach

Lakshminarayana

Kammoun

Debbah

et al. 2021

IEEE Trans. Smart Grid

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We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheless bypass the BDD with high probability. The algorithm design is guided by results from random matrix theory. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings.

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Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid

Cited by 9 publications

References 19 publications

Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Moving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids

Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach

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