Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach

TL;DR

This paper presents a machine learning-based method using XGB Classifier to assess and classify power system disturbances caused by cyber-attacks, natural events, or no events, enhancing real-time security monitoring.

Contribution

It introduces a novel application of XGB Classifier on a specific power system dataset for real-time disturbance assessment in smart grids.

Findings

The model accurately classifies attack, natural, and no-events.

Good performance metrics across all sub-datasets.

Effective for real-time cybersecurity assessment.

Abstract

Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, as a countermeasure, an assessment solutions with machine learning capabilities to match up in real-time, with the growth and fast pace of these cyber-attacks, is not only critical to the security, reliability and safe operation of power system, but also germane to guaranteeing advanced monitoring and efficient threat detection. Using the Mississippi State University and Oak Ridge National Laboratory dataset, the study used an XGB Classifier modeling approach in machine learning to diagnose and assess power system disturbances, in terms of Attack Events, Natural Events and No-Events. As test results show, the model, in all the three sub-datasets, generally demonstrates good performance on all metrics, as it relates to accurately identifying and classifying all the three power system events.