False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

Qu, Zhaoyang; Dong, Yunchang; Qu, Nan; Li, Huashun; Cui, Mingshi; Bo, Xiaoyong; Wu, Yun; Mugemanyi, Sylvere

doi:10.3389/fenrg.2021.644489

Cited by 27 publications

(8 citation statements)

References 23 publications

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“…Attempts to tamper with smart grid power transmission systems by introducing false data are called "false data injection attacks" (Qu et al, 2021). Data-driven machine learning is used in this work (Ashrafuzzaman et al, 2020) to combat state estimation assaults.…”

Section: Related Workmentioning

confidence: 99%

Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Aziz¹,

Irshad

Haider

et al. 2022

Front. Energy Res.

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False data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the six supervised learning (SVM-FS) hybrid techniques using the six different boosting and feature selection (FS) methodologies. A dataset from the smart grid is utilised in the process of determining the applicability of various technologies. Comparisons of detection strategies are made based on how accurately each one can identify different kinds of threats. The performance of classification algorithms that are used to detect FDI assaults is improved by the application of supervised learning and hybrid methods in a simulated exercise.

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Section: Related Workmentioning

confidence: 99%

Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Aziz¹,

Irshad

Haider

et al. 2022

Front. Energy Res.

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“…The current research is devoted to the risk assessment of network attacks and minimizes the impact of network attacks on the physical power grid (Srivastava et al, 2018). The genes extraction model was proposed in Qu et al (2021) to extract the key characteristics of FDIA and accurately identify FDIA through the uniqueness of the FDIA genes. In addition to network attacks, self-failure of communication network or other outside interference may also lead to data transmission error, transmission delay, transmission interruption or transmission dislocation.…”

Section: Physical Power Gridmentioning

confidence: 99%

Review of Modeling and Simulation Methods for Cyber Physical Power System

et al. 2021

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With the development of informatization and intellectualization technology, the power system has become the cyber-physical power system (CPPS) with deep integration of the cyber side and the physical side. As the support system of smart grids with new forms and features, the CPPS considers the coupling relationship between the communication network and the physical power grid, and is a research hotspot in the field of smart grids. This paper provides an overview of the CPPS framework and describes the interaction between the communication network and the physical power grid. Then the mainstream modeling and simulation methods of CPPS are summarized, and the advantages and disadvantages of each method are pointed out. In the end, the paper looks forward to the possible research directions of CPPS in the future. The article provides researchers with different perspectives on the dynamic model and simulation methods of CPPS.

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“…On the other hand, the command 'injection' denotes different types of server-issued instructions [7]. FDIA is one of the common attacks launched at any type of critical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

Murthy¹,

Udayakumar²,

Alenezi³

et al. 2023

Computer Systems Science and Engineering

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The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets' vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition (COADL-FDIAR) model for the IoT environment. The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment. To accomplish this, the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test. To detect and classify false data injection attacks, the Stacked Long Short-Term Memory (SLSTM) model is exploited in this study. Finally, the COA algorithm effectively adjusts the SLTSM model's hyperparameters effectively and accomplishes a superior recognition efficiency. The proposed COADL-FDIAR model was experimentally validated using a standard dataset, and the outcomes were scrutinized under distinct aspects. The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

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False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

Cited by 27 publications

References 23 publications

Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Review of Modeling and Simulation Methods for Cyber Physical Power System

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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