Toward a Gaussian-Mixture Model-Based Detection Scheme Against Data Integrity Attacks in the Smart Grid

In recent years, the smart grid has been recognized as an important form of the Internet of Things application. In the smart grid, as an energy-based cyber-physical system, the advanced metering infrastructure (AMI) will be developed to monitor and control the power grid by integrating computing and networking components to ensure stable and efficient operation. The AMI is vulnerable to cyber attacks, especially data integrity attacks. There have been a number of research efforts on detecting such attacks. Nonetheless, most of existing schemes either rely on predefined thresholds or require external knowledge. This may lead to low detection accuracy when the thresholds are improperly defined, and where there is a lack of the external knowledge. To address these issues, in this paper, we propose a Gaussian-mixture model-based detection scheme to mitigate data integrity attacks. Not relying upon the predefined 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. To evaluate the effectiveness of our proposed scheme, we conduct performance simulation based on the ElectricityLoadDiagrams20112014 data set, and then analyze the effectiveness of the proposed scheme with respect to detection accuracy and overhead. The results of our investigation show that our scheme could achieve a higher detection rate, and a lower error rate, in comparison to existing schemes based on the Min-Max model.