Big Data-Driven Detection of False Data Injection Attacks in Smart Meters

Ünal, Fatih; Almalaq, Abdulaziz; Ekici, Sami; Glauner, Patrick

doi:10.1109/access.2021.3122009

Cited by 25 publications

(14 citation statements)

References 36 publications

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“…Much research has been done using the data-driven approach using data preparation classifier modelling. For example, a data-driven technique is applied in reference [12] using machine learning, deep learning, and parallel computing techniques to detect malicious electricity users. A Turkish smart grid dataset is chosen for the model implementation and detection of false data injection.…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Ali,

Khan,

Javaid

et al. 2024

IET Generation Trans & Dist

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The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

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Section: Literature Reviewmentioning

confidence: 99%

“…Bernoulli naive Bayes classifier (NBC) uses naive Bayes theorem for independent feature identification [43]. The mathematical form of Bayes rule is stated in Equation (12).…”

Section: Bernoulli Naive Byes Classifiermentioning

confidence: 99%

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Ali,

Khan,

Javaid

et al. 2024

IET Generation Trans & Dist

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“…Extensive academic and industrial investigations have been carried out on cyber-attacks and their effects on smart grid networks. The research community has developed different methods of detecting these attacks and has studied the failure propagations they have caused [12], [13], [18]- [21].…”

Section: Related Workmentioning

confidence: 99%

“…The Temporal Failure Propagation Graph (TFPG) approach is utilized in the second stage to build attack pathways for detecting attack events. In [21], the authors proposed an attack detection framework using supervised machine-learning and deep learning algorithms to assess secured and unsecured networks.…”

Section: Related Workmentioning

confidence: 99%

Modeling Cascading Failures in Coupled Smart Grid Networks

et al. 2022

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The smart grid connects components of power systems and communication networks in an interdependent two-way system that delivers electricity to consumers and collects data that enables it to react to usage levels and interference from threats, such as cyber-attacks. In this paper, we propose a novel cyber-attack failure propagation model in smart grids. Our realistic failure propagation model addresses the system's heterogeneity by assigning different roles to its components. We define rules for and interdependencies of failure propagation and propose a new approach to studying cascading failures. In addition, our graph model identifies the most-vulnerable nodes. The model implements power flow analysis to guarantee that all transmission lines work below capacity and remove lines exceeding capacity. The model also considers that control packets could encounter different delays regarding the communication network structure and investigates the impact of communication delay on the failure of power components. Our results establish that by considering both power and communication characteristics and interdependencies, cascading failures can be modeled more accurately. We show that when we run the power flow analysis, there are a negligible number of failed nodes, which means that our model accurately identifies system failures.

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“…In this context, investigations on false data injection attacks (FDIAs) on smart grids have gained relevance. Reference [14] highlights that most of the attacks on smart grids usually lead to false data injection; then, many detection algorithms are developed, as in [15][16][17][18], while evaluating the effects of false data on power systems is a less investigated topic. Moreover, FDIA detection algorithms are often tested against manually calculated anomalous profiles that do not consider probabilistic scenarios, as in [17].…”

Section: Introductionmentioning

confidence: 99%

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

Bragatto

Bucarelli

et al. 2023

Sensors

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The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions.

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Big Data-Driven Detection of False Data Injection Attacks in Smart Meters

Cited by 25 publications

References 36 publications

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Modeling Cascading Failures in Coupled Smart Grid Networks

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

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