首页|Researchers at University of Kebangsaan Target Machine Learning (A Review On Mac hine Learning Techniques for Secured Cyberphysical Systems In Smart Grid Networ ks)

Researchers at University of Kebangsaan Target Machine Learning (A Review On Mac hine Learning Techniques for Secured Cyberphysical Systems In Smart Grid Networ ks)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Bangi, Malaysia, by NewsRx journalists, research stated, “The smart grid (SG) is an advanced cyber-p hysical system (CPS) that integrates power grid infrastructure with information and communication technologies (ICT). This integration enables real-time monitor ing, control, and optimization of electricity demand and supply.” The news reporters obtained a quote from the research from the University of Keb angsaan, “However, the increasing reliance on ICT infrastructures has made the S G-CPS more vulnerable to cyberattacks. Hence, securing the SG-CPS from these thr eats is crucial for its reliable operation. In recent literature, machine learni ng (ML) techniques and, more recently, deep learning (DL) techniques have been u sed by several studies to implement cybersecurity countermeasures against cybera ttacks in SG-CPS. Nevertheless, the achieving high performance of these state-of -the-art techniques is constrained by certain challenges, including hyperparamet er optimization, feature extraction and selection, lack of models’ transparency, data privacy, and lack of real-time attack data. This paper reviews the advance ment in using ML and DL techniques for cybersecurity countermeasures in SG-CPS. It analyzes the constraints that need to be addressed to improve performance and achieve real-time implementation. The various types of cyberattacks, cybersecur ity requirements, and security standards and protocols are also discussed to est ablish a comprehensive understanding of the cybersecurity context in SG-CPS.”

BangiMalaysiaAsiaCybersecurityCy borgsEmerging TechnologiesMachine LearningUniversity of Kebangsaan

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.4)