A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid

TL;DR

This paper compares four data mining algorithms used in intrusion detection systems for smart grids, highlighting that Random Forest performs best across multiple performance metrics.

Contribution

It provides a detailed performance evaluation of four algorithms, identifying Random Forest as the most effective for smart grid intrusion detection.

Findings

Random Forest has the highest detection probability.

Random Forest exhibits the lowest false alarm rate.

Random Forest achieves the best overall accuracy.

Abstract

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. An evaluation of performance of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.