Adaptive statistical detection of false data injection attacks in smart grids

Kallitsis, Michael; Bhattacharya, Shrijita; Stoev, Stilian; Michailidis, George

doi:10.1109/globalsip.2016.7905958

Cited by 14 publications

(4 citation statements)

References 24 publications

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“…Vector Autoregression (VAR) [8], and Kalman filters (KF) [9] are two such examples. For Quasi-static models, some examples include using Media Filtering (MF) [10] and Kriging Estimator (KE) [11]. This work is more concerned with the state-of-the-art deep learning approaches for FDIA detection, and those approaches fall majorly in the category of data-driven algorithms.…”

Section: Model-based Algorithms For Fdia Detectionmentioning

confidence: 99%

Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids

Nasir,

Talib,

Arshad

et al. 2024

Preprint

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False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we utilize multi-layer perceptron (MLP), convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models for the detection of the exact location of the attack node. All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Finally, the performance of each model was quantified on different variants of the dataset – which varied in their 𝑙2-norm.

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Section: Model-based Algorithms For Fdia Detectionmentioning

confidence: 99%

Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids

Nasir,

Talib,

Arshad

et al. 2024

Preprint

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“…In [149], the authors model the False Data Injection Attack, enabling the design of model-based detection schemes. The authors in [51][52][53][54][55][56][57][58][59][60][61] present static stateestimation techniques. In [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77], some dynamic state-estimation techniques are presented.…”

Section: Potential Countermeasures For Availability Threatsmentioning

confidence: 99%

Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid

et al. 2022

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The convergence of Information Technologies and Operational Technology systems in industrial networks presents many challenges related to availability, integrity, and confidentiality. In this paper, we evaluate the various cybersecurity risks in industrial control systems and how they may affect these areas of concern, with a particular focus on energy-sector Operational Technology systems. There are multiple threats and countermeasures that Operational Technology and Information Technology systems share. Since Information Technology cybersecurity is a relatively mature field, this paper emphasizes on threats with particular applicability to Operational Technology and their respective countermeasures. We identify regulations, standards, frameworks and typical system architectures associated with this domain. We review relevant challenges, threats, and countermeasures, as well as critical differences in priorities between Information and Operational Technology cybersecurity efforts and implications. These results are then examined against the recommended National Institute of Standards and Technology framework for gap analysis to provide a complete approach to energy sector cybersecurity. We provide analysis of countermeasure implementation to align with the continuous functions recommended for a sound cybersecurity framework.

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“…FDIAs in power grids were first introduced a decade ago by [7], which showed that an attacker with enough knowledge of the grid topology can design an unobservable attack that satisfies the power flow equations and bypasses the BDD module. Influential reference [7] prompted an increased interest in detection of FDIAs [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…Survey [26] classifies the FDIA detection algorithms into two categories: model-based methods [12], [13], [14], [15], [16] and data-driven methods [17], [18], [19], [20], [21]. In general, model-based algorithms require first to build a system model and estimate its parameters to detect FDIAs.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

Boyaci,

Umunnakwe,

Sahu

et al. 2021

Preprint

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False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grid. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and a publicly accessible dataset for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement data. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in literature that our algorithm outperforms the best available solutions by 6.21%, 0.69%, and 2.73% in detection rate and by 3.65%, 0.34% and 1.38% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively. NOMENCLATURE P i + jQ i Complex power injection at bus i. P ij + jQ ij Complex power flow between bus i and j. V i , θ i Voltage magnitude and phase angle of bus i. θ ij θ i − θ j . G ij + jB ij ijth elements of bus admittance matrix. g ij + jb ij Series branch admittance between bus i and j. g si + jb si Shunt branch admittance at bus i.Original state vector without an attack. x ∈ R n False data injected state vector. z ∈ R m A measurement vector. z o ∈ R m Original measurement vector. z a ∈ R m Attacked measurement vector. h(x)Nonlinear measurement function at x. H ∈ R m×n Jacobian matrix.

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Adaptive statistical detection of false data injection attacks in smart grids

Cited by 14 publications

References 24 publications

Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids

Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids

Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid

Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

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