Run-Time Prediction Practices of Multimedia Web Design in Technology Management

Hu, Bin; Noman, Sohail M.; Irshad, Muhammad; Tang, Xilang; Chen, Song; Muhammad, Musa Uba

doi:10.1007/978-981-16-3180-1_23

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Although the smart grid uses communication and information technology to generate, distribute, and consume electricity, there are potential disadvantages such as compromised reliability during power outages and potential privacy concerns if critical data is lost or stolen [36,37] . One growing method of cyberattack against smart grids is FDI, which can be difficult to detect using current methods [38,39]. As an alternative to FDI detection, machine learning has been proposed.…”

Section: Research Backgroundmentioning

confidence: 99%

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

Mohammed,

Al-Jumaily,

Singh

et al. 2024

IEEE Access

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The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper's findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

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Section: Research Backgroundmentioning

confidence: 99%

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

Mohammed,

Al-Jumaily,

Singh

et al. 2024

IEEE Access

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“…Since these problems have been discovered, the smart grid has gained the interest of both government agencies and private sector companies. An increasing number of attacks against smart grids are being carried out using a technique known as False Data Injection (FDI) (Hu et al, 2021).…”

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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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An Adaptive Approach of Natural Language Processing (NLP) to Predict Aggressive Behavior of Adults in Educational Institution

Ain

Irshad

et al. 2023

Learning and Analytics in Intelligent Systems

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Run-Time Prediction Practices of Multimedia Web Design in Technology Management

Cited by 3 publications

References 26 publications

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

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

An Adaptive Approach of Natural Language Processing (NLP) to Predict Aggressive Behavior of Adults in Educational Institution

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