A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost

•Electricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids.•The detection of fraudulent consumers becomes difficult when there is a large amount of data.•Smart grids can be used to solve this problem as it provides a two-way electricity flow which allows someone to detect, reenact and apply new changes to the electric data flow.•An ensemble model based on convolutional neural network and extreme gradient boosting (CNN-XGB) model is proposed.