Study Results from National Institute of Technology Raipur Update Understanding of Engineering (Intelligent Intrusion Detection Scheme for Smart Power-grid Using Optimized Ensemble Learning On Selected Features)

2022 DEC 06 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Fresh data on Engineering are presented in a new report. According to news reporting originating from Raipur, India, by NewsRx correspondents, research stated, “The smart grid has gained a reputation as the advanced paradigm of the power grid. It is a complicated cyberphysical system that combines information and communication technology (ICT) with a traditional grid that can remotely control operations.”

Our news editors obtained a quote from the research from the National Institute of Technology Raipur, “It provides the medium for exchanging real-time data between the company and users through the advanced metering infrastructure (AMI) and smart meters. However, smart grids have many security and privacy concerns, such as intruding sensitive data, firmware hijacking, and modifying data due to the high reliance on ICT. To protect the power-grid system from these counteracts and for reliable and efficient power distribution, early and accurate identification of these issues needs to be addressed. The intrusion detection in a smart grid system plays an essential role in providing a secure service and transmitting the high priority alert message to the system admin about the detection of adversary attacks. This paper proposes an intelligent intrusion detection scheme to accurately classify various attacks on smart power grid systems. The proposed scheme used the binary grey wolf optimization-based feature selection. It optimized the ensemble classification approach to learn the non-linear, overlapping, and complex electrical grid features taken from publicly available Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) dataset. The experimental results using a 10-fold cross-validation setup and selected feature subset for two class and three class problems reveal the proposed method’s promising performance.”

According to the news editors, the research concluded: “Further, the significantly superior performance compared to the existing benchmark methods justified the robustness of the proposed scheme.”

This research has been peer-reviewed.

For more information on this research see: Intelligent Intrusion Detection Scheme for Smart Power-grid Using Optimized Ensemble Learning On Selected Features. International Journal of Critical Infrastructure Protection, 2022;39:100567. International Journal of Critical Infrastructure Protection can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; International Journal of Critical Infrastructure Protection - http://www.journals.elsevier.com/international-journal-of-critical-infrastructure-protection/)

The news editors report that additional information may be obtained by contacting Manikant Panthi, National Institute of Technology Raipur, Dept. of Computer Applications, Raipur, India.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.ijcip.2022.100567. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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