Machine Learning Algorithm for Detection of False Data Injection Attack in Power System

Kumar, Ajit; Saxena, Neetesh; Choi, Bong Jun

doi:10.1109/icoin50884.2021.9333913

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…• Energy Sector: As we discussed earlier in this article machine learning has been heavily used in the energy sector. In [126] a comparative study using random forest, support vector, naive Bayes, decision tree, and AdaBoost was performed to predict false data injection attacks in power systems. The experiment performed showed random forest yielded the most accurate results with and without feature selection and is very effective in such types of problems.…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

et al. 2023

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In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social media platforms, healthcare systems, etc., there is a lot of data online today. Machine learning (ML) is something we need to understand to do smart analyses of these data and make smart, automated applications that use them. There are many different kinds of machine learning algorithms. The most well-known ones are supervised, unsupervised, semi-supervised, and reinforcement learning. This article goes over all the different kinds of machine-learning problems and the machine-learning algorithms that are used to solve them. The main thing this study adds is a better understanding of the theory behind many machine learning methods and how they can be used in the real world, such as in energy, healthcare, finance, autonomous driving, e-commerce, and many more fields. This article is meant to be a go-to resource for academic researchers, data scientists, and machine learning engineers when it comes to making decisions about a wide range of data and methods to start extracting information from the data and figuring out what kind of machine learning algorithm will work best for their problem and what results they can expect. Additionally, this article presents the major challenges in building machine learning models and explores the research gaps in this area. In this article, we also provided a brief overview of data protection laws and their provisions in different countries.

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Section: Applications Of Machine Learningmentioning

confidence: 99%

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

et al. 2023

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“…The authors in [67] used minority oversampling and feature selection to improve the detection of FCIAs on power systems using machine learning. The authors in [68] showed that the performance of various ML models to detect FCIAs is dependent on pre-processing and feature selection. Various ML models analyzed include Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), etc.…”

Section: A Artificial Intelligence-based Techniquesmentioning

confidence: 99%

Command Injection Attacks in Smart Grids: A Survey

Usama,

Aman

2024

IEEE Open J. Ind. Applicat.

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Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The False Command Injection Attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This paper presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of FCIAs on smart grids are also discussed. Subsequently, this paper provides an extensive review of detection studies, categorizing them based on the type of detection technique employed.

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“…Attacks in this category include replay and false injection. Studies in [56–59] have investigated these attacks and suggested prevention algorithms. These include cryptography, message verification, time stamp verification, and machine learning.…”

Section: Cyber Attacks In Cavsmentioning

confidence: 99%

Cyber security analysis of connected vehicles

Mwanje,

Kaiwartya,

Aljaidi

et al. 2024

IET Intelligent Trans Sys

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The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

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Machine Learning Algorithm for Detection of False Data Injection Attack in Power System

Cited by 15 publications

References 20 publications

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms

Command Injection Attacks in Smart Grids: A Survey

Cyber security analysis of connected vehicles

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