Graph theoretical defense mechanisms against false data injection attacks in smart grids

Ansari, M H Khadem; Vakili, Vahid Tabataba; Tavassoli, Parmiss

doi:10.1007/s40565-018-0432-2

Cited by 27 publications

(8 citation statements)

References 47 publications

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“…Maximise the average attack cost

ρ^{m} = \underset{ρ}{truemax} \frac{1}{m} \underset{k \in M}{false\sum} α_{k}

To minimise the protection cost, Bi and Zhang [79] framed the protection problem as a variant of the Steiner tree problem in a graph. Given an undirected graph with non‐negative edges and a set of vertices, which represent transmission lines and buses in the power network, the Steiner tree problem entails finding a tree with minimum weights, which contains all the vertices [92]. To select the minimum set of meters to be protected, they propose two algorithms: a Steiner vertex enumeration algorithm and MILP.…”

Section: Defences Against Fdiasmentioning

confidence: 99%

Survey of machine learning methods for detecting false data injection attacks in power systems

Hu²,

et al. 2020

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“…Maximise the average attack cost

ρ^{m} = \underset{ρ}{truemax} \frac{1}{m} \underset{k \in M}{false\sum} α_{k}

Section: Defences Against Fdiasmentioning

confidence: 99%

Survey of machine learning methods for detecting false data injection attacks in power systems

Hu²,

et al. 2020

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“…In order to minimize the protection cost, Bi et al frame the protection problem as a variant of the Steiner tree problem in a graph [85]. Given an undirected graph with non-negative edges and a set of vertices which represent transmission lines and buses in the power network, the Steiner tree problem entails finding a tree with minimum weights which contains all the vertices [98]. To select the minimum set of meters to be protected, they propose two algorithms: a Steiner vertex enumeration algorithm and MILP.…”

Section: Defenses Against False Data Injection Attacksmentioning

confidence: 99%

A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems

Sayghe¹,

Hu²,

Zografopoulos³

et al. 2020

Preprint

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Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid monitoring systems. Adversaries can successfully perform FDIAs in order to manipulate the power system State Estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the Energy Management System (EMS) towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include Bad Data Detection (BDD) algorithms to eliminate errors from the acquired measurements, e.g., in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. In order to overcome the limitations of traditional residual-based BDD approaches, datadriven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This paper provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

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“…False data injection attack (FDIA) is a relatively common cyber attack, and it is highly challenging [7]. FDIAs fight the LFC system and bypass the error data detection of SCADA, and then lead to the state estimation bias and the wrong decisions of SCADA [8], which makes the system unstable and affects power economic dispatch [9]. Many research works have been devoted to studying the possible means of constructing FDIAs.…”

Section: Introductionmentioning

confidence: 99%

Detection of False Data Injection Attacks on Load Frequency Control System with Renewable Energy Based on Fuzzy Logic and Neural Networks

Chen

Zhu

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples.

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Graph theoretical defense mechanisms against false data injection attacks in smart grids

Cited by 27 publications

References 47 publications

Survey of machine learning methods for detecting false data injection attacks in power systems

Survey of machine learning methods for detecting false data injection attacks in power systems

A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems

Detection of False Data Injection Attacks on Load Frequency Control System with Renewable Energy Based on Fuzzy Logic and Neural Networks

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