A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System

TL;DR

This paper presents a two-layer hierarchical machine learning model that detects and classifies cyberattacks in smart grid systems with over 95% accuracy, enhancing cybersecurity measures.

Contribution

It introduces a novel layered approach that improves detection accuracy by focusing training on specific tasks, outperforming existing models.

Findings

Achieved 95.44% detection accuracy.

Outperformed recent cyberattack detection models.

Effective in distinguishing between normal and attack states.

Abstract

Modern smart grid systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyberattacks. The occurrence of a cyberattack has increased in recent years resulting in substantial damage to power systems. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. Automated detection of cyberattacks with high accuracy is a challenge. To address this, we propose a two-layer hierarchical machine learning model having an accuracy of 95.44 % to improve the detection of cyberattacks. The first layer of the model is used to distinguish between the two modes of operation (normal state or cyberattack). The second layer is used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we compared its performance against other recent cyber attack detection models proposed in the literature.