A Gaussian-Mixture Model Based Detection against Data Integrity Attacks in Smart Grid Poster.pdf

The goal of this project is to establish a theoretical and empirical foundation for secured and efficient energy resource management in the smart grid - a typical energy-based cyber-physical system and the future critical energy infrastructure for the nation. In this study, we focus on the detection threats and propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to mitigate data integrity attacks. Not relying upon the pre-defined thresholds or external knowledge, our developed scheme operates through narrowing the range of normal data, which can be obtained through clustering the historical data and learning minimum and maximum values or distance values to each center of individual clusters. The results of our investigation show that our scheme could achieve a high detection rate, and a low error rate.