Smart grid gives more control and information to the utility companies. However, it can be leveraged for data manipulation, which can lead to new techniques in electricity theft. This paper presents an electricity theft detection framework, designed for handling real-time large-scale smart grid data to address these new emerging threats. It uses a hybrid approach, combining the information inferred by analyzing the reported data from distribution transformer meters with machine learning algorithms to discover fraudulent activity. We added an additional form of attack to the six previously known patterns and generated malicious variants of consumption data to solve the problem of imbalanced dataset classes, resulting in more accurate classifiers. The framework also allows for a trade-off between the detection rate and triggered false alarms by using a sliding window in the decision-making process. In the end, the proposed framework is evaluated using well-known clustering and classification methods in a practical scenario, resulting in outcomes superior or equal to the previously achieved scores while having the advantages of online and distributed processing.

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