Abstract
Developments in information and communication technologies (ICT) achieved new trends such as smart grid (SG). Innovations in ICT bring challenges to our security and expectations of privacy. ICT has such a big attack surface that possess increased risks from cyber-attacks in smart grid. Actually, as a critical infrastructure, the smart grid is more sensitive to cyber-attacks, as monitoring and control of smart grid can be realized over standard internet-based protocols. Therefore, an attacker may achieve financial cost to the service and create loss of electric resources, through breaching the real-time balance between energy production and consumption, by controlling data produced by the smart entities. Therefore, in this study, bagging ensemble classifier is evaluated for intrusion detection in a smart grid with a benchmark data. Experimental results have revealed the practicality of Bagging ensemble classifier by realizing a better performance for the cyber security threats in a smart grid environment. The results indicated that the suggested Bagging ensemble classifier with REP Tree achieved a total classification accuracy of 99.94 %.