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Modern smart grids rely on advanced metering infrastructure (AMI) networksfor monitoring and billing purposes. However, such an approach suffers fromelectricity theft cyberattacks. Different from the existing research thatutilizes shallow, static, and customer-specific-based electricity theftdetectors, this paper proposes a generalized deep recurrent neural network(RNN)-based electricity theft detector that can effectively thwart thesecyberattacks. The proposed model exploits the time series nature of thecustomers' electricity consumption to implement a gated recurrent unit(GRU)-RNN, hence, improving the detection performance. In addition, theproposed RNN-based detector adopts a random search analysis in its learningstage to appropriately fine-tune its hyper-parameters. Extensive test studiesare carried out to investigate the detector's performance using publiclyavailable real data of 107,200 energy consumption days from 200 customers.Simulation results demonstrate the superior performance of the proposeddetector compared with state-of-the-art electricity theft detectors.

Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters