Title: Detection of cyber-attacks for sensor measurement data using supervised machine learning models for modern power grid system

Authors: Manikant Panthi; Tanmoy Kanti Das

Addresses: Department of Computer Application, National Institute of Technology Raipur, Chhattisgarh, 492010, India ' Department of Computer Application, National Institute of Technology Raipur, Chhattisgarh, 492010, India

Abstract: The smart power grid systems are continually exposed to malicious cyber-attacks that are difficult to detect. If smart power grid attacks are not identified quickly and correctly, they may cause substantial economic losses and damage to the power system. To enhance productivity and improve the security of the smart power grid system against cyber-attacks, real-time detection of smart power grid attacks is still challenging. In recent years, there have been more cyberattacks, which have caused a lot of damage to power systems. This paper presents an experimental investigation of seven different approaches for detecting malicious activities and cyberattacks in the smart power grid system. Further, we employed maximum relevancy and minimum redundancy-hesitant fuzzy set feature selection technique to boost the attack detection performance. The experimental results demonstrate that random forest achieved the highest performance and average accuracy for two-class (95.30%) and three-class (95.33%) classifications, which shows that the presented proposed Model notably outperformed the other cyber-attack detection models.

Keywords: SCADA; MRMR-HFS; cyber-attacks; machine learning.

DOI: 10.1504/IJCCBS.2023.136320

International Journal of Critical Computer-Based Systems, 2023 Vol.10 No.4, pp.330 - 354

Received: 23 Oct 2022
Accepted: 12 Feb 2023
Published online: 30 Jan 2024 *