A Robust Ensemble Model for Cyber Security Threats Analysis in Smart Grids

Abstract

Changes in life style and technology tend us to adapt to the smart systems easily. Utilization of smart systems everywhere through mobile devices, laptops and home pc are now become flexible. The user friendly approach and simple steps involved web applications helpful for us also grab the user information in single swipe. The increase in web usage also increase the web application cyber threats to be happening in most of the third party connectivity websites. A Novel and robust approach on detecting the threats present in the smart electricity grids is presented here. The research framework focused on hybrid the two robust algorithms (RCTD) by associating ensemble learning approach together with adversial pattern Network (APN) approach to create a robust architecture that detect the threats and alert the end user on miscellaneous activity. The proposed model utilizes MATGUI interface for frond end with user login and admin login. The presented system approach uses a random number of user data(UD) of electrical consumption and stampings for testing. The proposed approach is proved to achieve >90% of accuracy.