Network intrusion detection in smart grid using supervised learning
Jason Marandi, Priyanka Ahlawat · International Journal of Reasoning-based Intelligent Systems · 2025
The emergence of smart grid (SG) as a replacement for the traditional power grid has increased the efficiency and reliability of the power grids. This is due to incorporating communication networks with the traditional power grid. This integration also exposes the grid to vulnerabilities common in communication networks. An intrusion detection system (IDS) is an important tool for providing secure and reliable services in smart grid. Anomaly-based IDS (AIDS) has gained importance for detecting zero-day attacks. Machine learning (ML) is a promising solution as it learns from past events to predict new network threats. Two common ML types are supervised and unsupervised learning. They differ based on how data is presented to them. This paper explores supervised ML approaches using the CICIDS2017 dataset to achieve the best classifier and parameters. The tuned random forest (RF) model achieved an impressive 99.92% accuracy with a 70:30 dataset split for training and testing.