Machine Learning to detect cyber-attacks and discriminating the types of
  power system disturbances
Diane Tuyizere Remy Ihabwikuzo
This research proposes a machine learning-based attack detection model for
power systems, specifically targeting smart grids. By utilizing data and logs
collected from Phasor Measuring Devices (PMUs), the model aims to learn system
behaviors and effectively identify potential security boundaries. The proposed
approach involves crucial stages including dataset pre-processing, feature
selection, model creation, and evaluation. To validate our approach, we used a
dataset used, consist of 15 separate datasets obtained from different PMUs,
relay snort alarms and logs. Three machine learning models: Random Forest,
Logistic Regression, and K-Nearest Neighbour were built and evaluated using
various performance metrics. The findings indicate that the Random Forest model
achieves the highest performance with an accuracy of 90.56% in detecting power
system disturbances and has the potential in assisting operators in
decision-making processes.