Title: Analysis of smart grid-based intrusion detection system through machine learning methods

Authors: D. Ravikumar; K. Sasikala; R.S. Vijayashanthi; S. Narasimha Prasad

Addresses: Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, India ' Department of Electrical and Electronics Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, India ' Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai-77, India ' Department of Electronics and Communication Engineering, Dhanalakshmi College of Engineering, Anna University, Chennai, India

Abstract: This article aims to maximise network strong security and its enhancement by presenting different preventative strategies since intrusion detection is essential to computer network security challenges. In this study, intrusion detection is addressed as a challenge of extracting outliers that use the network behaviour dataset, and semi-supervised classification technique based on shared closest neighbours are suggested. First, we provide a thorough explanation of the fundamentals of cyber security assaults, supervised machine learning methods, and intrusion detection systems. Then, we discuss pertinent initiatives related to the use of supervised methods for intrusion detection. Finally, a taxonomy based on these connected works is offered. This article attempts to offer a sophisticated and distinctive intrusion detection model capable of categorising electrical network events and CDs for smart grids into binary-class, trinary-class, and multiple-class categories. As an effective machine learning model for intrusion detection, it employs the grey wolf algorithm (GWA).

Keywords: databases; support vector machines; smart grids; cyber attacks; intrusion detection systems; IDS.

DOI: 10.1504/IJESDF.2024.136015

International Journal of Electronic Security and Digital Forensics, 2024 Vol.16 No.1, pp.84 - 96

Received: 18 Jul 2022
Accepted: 05 Oct 2022
Published online: 12 Jan 2024 *

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