Ensemble learning methods for power system cyber-attack detection

Power system is one of the most important industrial control systems in today's society. In recent years, power systems have been well researched and developed extensively with a high rate. In order to optimally integrate systems and reduce costs, lots of advanced information technologies are involved into power systems. Traditional power system is changing to the smart power grid rapidly. Therefore, modern power systems are now exposing to the public network and information security is becoming a new threat to resilience. In this work, we explore the suitability of ensemble learning methods as a means of detecting power system cyber-attack. We evaluate various ensemble learning methods as cyber-attack detectors and discuss the practical implications for deploying ensemble learning methods as an enhancement to existing power system architectures.