Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation

Ashok, Aditya; Manimaran, G.; Ajjarapu, Venkataramana

doi:10.1109/tsg.2016.2596298

Cited by 214 publications

(101 citation statements)

References 17 publications

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“…In this section, the proposed approach is applied and investigated on the IEEE 14-bus test grid. This test grid is often used in the context of FDI attack detection [27], [29], [30] and attack construction [35], [38]. The voltage angles and magnitudes of this grid are calculated from the AC power flow solver implemented by the pandapower-tool [55].…”

Section: Case Studymentioning

confidence: 99%

“…Seeking to derive ways of rendering the smart grid robust to FDI attacks, the authors of [14] review major types of defense strategies, such as data authentication [15], and the inclusion of time synchronized phasor measurement units (PMUs) [29], [30]. In these works, the measurements originating from PMUs arXiv:1810.04894v4 [eess.SP] 23 Aug 2019 are considered to be protected against attacks, and thus can be used as a trustworthy reference.…”

mentioning

confidence: 99%

“…However, this approach is easily corrupted, especially if the attack is launched with small gradual changes. Other detection methods for undetectable FDI attacks require reliable load forecasts [30], or explore machine learning concepts [32]. The main problem with machine learning approaches, such as in [32], is that they need a large data set of historic grid states that also contains attacked states.…”

mentioning

confidence: 99%

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

Drayer

Routtenberg

2020

IEEE Systems Journal

125

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Section: Case Studymentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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

Drayer

Routtenberg

2020

IEEE Systems Journal

125

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“…Techniques proposed to deal with stealthy attacks include statistical methods such as sequential detection using Cumulative Sum (CUSUM)-type algorithms [16], and measurements consistency assessment under certain observability assumptions [35]. A detection method that leverages online information is described in [3], which is applicable by ensuring the availability and accuracy of load forecasts and generation schedules. In [19], a mechanism is introduced to formulate the detection scheme as a matrix separation problem, but it only recovers intrusions among corrupted measurements over a particular period of time.…”

Section: Introductionmentioning

confidence: 99%

From Static to Dynamic Anomaly Detection With Application to Power System Cyber Security

Pan

Pálenský

Esfahani

2020

IEEE Trans. Power Syst.

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Developing advanced diagnosis tools to detect cyber attacks is the key to security of power systems. It has been shown that multivariate data injection attacks can bypass bad data detection schemes typically built on static behavior of the systems, which misleads operators to disruptive decisions. In this article, we depart from the existing static viewpoint to develop a diagnosis filter that captures the dynamics signatures of such a multivariate intrusion. To this end, we introduce a dynamic residual generator approach formulated as robust optimization programs in order to detect a class of disruptive multivariate attacks that potentially remain stealthy in view of a static bad data detector. We investigate two possible desired features: (i) a non-zero transient and (ii) a non-zero steady-state behavior of the residual generator in the presence of an attack. In case (i), the problem is reformulated as a finite, but possibly non-convex, optimization program.We further develop a linear programming relaxation that improves the scalability, and as such practicality, of the diagnosis filter design. In case (ii), it turns out that the resulting robust program admits an exact convex reformulation, yielding a Nash equilibrium between the attacker and the residual generator. This assertion has an interesting implication: the proposed approach is not conservative in the sense that the additional knowledge of the worst-case attack does not improve the diagnosis performance. To illustrate our theoretical results, we implement the proposed diagnosis filter to detect multivariate attacks on the system measurements deployed to generate the so-called Automatic Generation Control signals in a three-area IEEE 39-bus system. P.MohajerinEsfahani}@tudelft.nl).

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“…In power transmission systems, the popular method used to detect bad measurement data is the static state estimator (SSE) based on weighted least squared (WLS). However, SSE can be manipulated by attackers if the power network topology is known [10]- [12]. Therefore, the cyberattacks stay undetected as the attack indicator (control/measurement residuals) of the method is kept under predefined detected level.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Dynamic State Estimation for Cyber Intrusion Detection in Networked DC Microgrids

Nguyen

Ngo³

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if there is manipulated data received from their neighboring microgrids for control purposes. A dynamic model supporting the dynamic state estimation will be constructed for the networked microgrids. The optimal distributed state estimation, which is robust to load disturbances but sensitive to false data injected from neighboring microgrids will be presented. To demonstrate the effectiveness of the proposed approach, we simulate a 12kV three-bus networked DC microgrids in MATLAB/Simulink. Residual information corresponding to the false data injected from neighbors validates the efficacy of the proposed approach in detecting compromised agents of neighboring microgrids.

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Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation

Cited by 214 publications

References 17 publications

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

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

From Static to Dynamic Anomaly Detection With Application to Power System Cyber Security

Distributed Optimal Dynamic State Estimation for Cyber Intrusion Detection in Networked DC Microgrids

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