Partial grid false data injection attacks against state estimation

Margossian, Harag; Sayed, Mohammad Ali; Fawaz, Wissam; Nakad, Zahi

doi:10.1016/j.ijepes.2019.03.039

Cited by 44 publications

(14 citation statements)

References 25 publications

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“…The simulation results demonstrated that by correctly estimating one state of the system, the FDI attack could be performed without being detected by BDD methods. In [40], the impact of FDI attacks on estimated states of power grids was investigated assuming attackers have partial knowledge of the system information. Then, the partial grid FDI attack was applied to demonstrate how FDI attacks could be undetectable.…”

Section: State Estimation Methodsmentioning

confidence: 99%

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Mohammadi

2021

Energies

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In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discussed.

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Section: State Estimation Methodsmentioning

confidence: 99%

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Mohammadi

2021

Energies

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“…Liu and He utilized the most commonly used weighted last square (WLS) approach [12]. Furthermore, advanced information technology offers additional opportunities [13], as well as innovative architectures [14,15]. The current level of observability (lack of measurement devices and expert systems) makes effective fault localization difficult.…”

Section: Literature Review Of Dsse Use-casesmentioning

confidence: 99%

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

et al. 2021

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Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

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“…S A } is synchronously constructed, where the attacked bus is randomly selected in {bus 2, bus 3, bus 9} and the corresponding attack intensity is randomly selected from {10%, 20%, 30%}. The rationale behind such settings mainly includes the following two points: 1) The number of tampered variables should be limited due to the presence of attack cost [29]. 2) 30% is taken as the upper bound of the attack intensity since the normal distribution of the measurements is well known by the operators/defenders in the control center, indicating an attack vector with an overly large attack intensity is fairly doubtful.…”

Section: A Data Generationmentioning

confidence: 99%

Detecting False Data Injection Attacks in Smart Grids with Modeling Errors: A Deep Transfer Learning Based Approach

Guo

Chen

et al. 2021

Preprint

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Most traditional false data injection attack (FDIA) detection approaches rely on static system parameters or a single known snapshot of dynamic ones. However, such a setting significantly weakens the practicality of these approaches when facing the fact that the system parameters are dynamic and cannot be accurately known during operation due to the presence of uncertainties in practical smart grids. In this paper, we propose an FDIA detection mechanism from the perspective of transfer learning. Specifically, the known initial/approximate system is treated as a source domain, which provides abundant simulated normal and attack data. The real world's unknown running system is taken as a target domain where sufficient real normal data are collected for tracking the latest system states online. The designed transfer strategy that aims at making full use of data in hand is divided into two optimization stages. In the first stage, a deep neural network (DNN) is built by simultaneously optimizing several well-designed terms with both simulated data and real data, and then it is fine-tuned via real data in the second stage. Several case studies on the IEEE 14-bus power system verify the effectiveness of the proposed mechanism.

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Partial grid false data injection attacks against state estimation

Cited by 44 publications

References 25 publications

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

Detecting False Data Injection Attacks in Smart Grids with Modeling Errors: A Deep Transfer Learning Based Approach

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