In the smart grid, the Advanced Metering Infrastructure (AMI) will be deployed to monitor and control the power grid by integrating both computing and networking components to achieve stable and efficient operation. The AMI is vulnerable to cyber attacks, especially in the form of data integrity attacks. A number of research efforts have been devoted to detecting such attacks. Nonetheless, the majority of existing schemes either rely on a pre-defined threshold, or require external knowledge. This leaves open the possibility for low detection accuracy when the threshold is improperly defined, and where there is a lack of the requisite external knowledge. To address this issue, in this paper we propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to combat data integrity attacks. Not relying upon the pre-defined threshold or external knowledge, our scheme operates by narrowing the range of normal data that can be obtained by clustering the historical data and learning the minimum and maximum values of individual clusters. To validate the effectiveness of our scheme, we conduct performance evaluation based on the ElectricityLoadDiagrams20112014 data set, and analyze the effectiveness of the proposed scheme with respect to detection accuracy.The results of our investigation demonstrate that our scheme can achieve a higher detection rate, and lower error rate, in comparison with existing schemes based on the Min-Max model.