Electricity Theft Detection in Smart Meters Using a Hybrid Bi-directional GRU Bi-directional LSTM Model

In this paper, a problem of misclassification due to cross pairs across a decision boundary is investigated. A cross pair is a junction of the two opposite class samples. These cross pairs are identified using Tomek links technique. The majority class sample associated with cross pairs are removed to segregate the two opposite classes through an affine decision boundary. Due to non-availability of theft data, six theft cases are used to synthesize theft data to mimic real world scenario. These six theft cases are applied to benign class data, where benign samples are modified and malicious samples are synthesized. Furthermore, to tackle the class imbalance issue a K-means SMOTE is used for the provision of balance data. Moreover, the technical route is to train the model on a timeseries data of both classes. Training model on imbalance data tends to misclassification of the samples, due to biasness towards a majority class, which results in a high FPR. The balanced data is provided as an input to a hybrid bi-directional GRU and bi-directional LSTM model. The two classes are efficiently classified with a high accuracy, high detection rate and low FPR.