The Deployment of Smart Grid requires consideration of all the security parameters in the entire architecture of Smart Grid. Data Security
and Communication Security are the major milestones in security that need to be addressed in the present scenario in Smart Grid. In this
paper we model a Deep Belief Network to detect the normal and abnormal behaviors in the traffic pattern of Smart Grid data. Deep belief
Network has been deployed to identify the anomalies in the Smart Grid data traffic thereby detecting intrusion .Support Vector Machine has
been used for intrusion classification after creating the Deep Belief Network Model. Using SVM model with deep belief networks has helped
in reduction of data complexity and also in identifying the core features to be considered for the implementation of Intrusion detection in
Smart Grid Model.
Dr. Radhika N. and Menon, D. M., “A secure deep belief network architecture for intrusion detection in smart grid home area network”, IIOAB Journal, vol. 7, pp. 479-483, 2017.