An RTDS-based Early-stage Attack Detection Mechanism for Smart Grid Networks

The smart grid is a modern system that connects various elements and presents unique security issues, such as cascading failures caused by power system component failure or cyber attacks. To address this problem, machine learning algorithms are increasingly being employed to identify and predict such failures. In this paper, we propose a new mechanism that uses supervised machine learning algorithms to detect early-stage failures in smart grid networks. We use a realistic methodology to create a dataset to train the algorithms, and the mechanism can detect failures at the early-stages of propagation. To improve detection accuracy, we use the eXtreme Gradient Boosting (XGBoost) algorithm and consider both power and communication network features. The mechanism’s efficacy is evaluated using the IEEE 14-bus system.