Cyber Attacks Detection using Machine Learning in Smart Grid Systems

Abstract

Smart grid systems provide reliable and efficient power through a smart information and communication technology. Reliability of smart grid systems are of great importance as any critical issue in the system will affect several millions of device connected through communication network. The reliability of smart grid can be compromised by cyber attacks. This entails continuous cyber security monitoring for smart grid systems. In this work, a machine learning-based cyber attacks detection is proposed. The proposed mechanism is shown to identify false-data injection attaks which is one of the most substantial attacks in smart grid systems. In the proposed scheme, we generated the datasets using an IEEE-34 bus system with cyber attacks implementation. In addition, we have shown that our machine learning models can succesfully identify the attack in smart grid systems.