Abstract
The advent and development of the smart grid have introduced a number of opportunities for improving efficiencies and overall performance. Along with the opportunities, each technology wave brings with it new opportunities for attackers to perpetrate damage, that in the smart grid scenario afflict electricity networks i.e. a cyber physical environment. In this paper we propose a method aimed to detect intrusion in smart grid by exploiting supervised machine learning techniques. By real-time log analysis, obtained from SCADA systems, widely exploited in a plethora of industrial applications, we extract a set of 26 features to build seven different machine learning models. Experimental analysis performed with 7 machine learning algorithms shows the effectiveness of the proposed method.
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