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In this article, we investigate the problem of electricity theft attacks on smart meters when malicious customers (i.e., adversaries) claim injecting more generated energy into the grid to get more profits from utility companies. These attacks can be applied by accessing the smart meters monitoring renewable-based distributed generation (DG), and manipulating the reading. In this article, we propose approaches that rely on data sources with only a single generator (i.e., solar only) and multifuel type; and address the crucial effects of slight perturbations that the attacker can add, which can deceive the detector. In particular, this article introduces an efficient multitask deep-learning-based detector that offers a higher detection rate, copes with different fuel types, and uses only single data sources. The proposed detector incorporates months and days as two additional features to boost the performance and properly guide the model to successful detection. The proposed method is then extended to consider small perturbations that attackers may use to launch successful attacks. We conduct extensive simulations for two different detectors, one for solar DG and the other for multiple fuel types (i.e., solar and wind). Using a realistic dataset, the results reveal that the proposed recurrent neural network-based detectors identify adversaries at a higher rate than the existing solutions, even with minimal perturbations and different fuel types.

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain