Robust Detection of Electricity Theft Against Evasion Attacks in Smart Grids

Electricity theft cyber-attacks pose significant threats to smart power grids. In these attacks, malicious customers hack into their smart meters and manipulate the integrity of their energy consumption readings to reduce their electricity bills. Recently, machine learning techniques have been successfully employed to detect such cyber-attacks. However, the developed detectors have been tested against simple attacks. In this paper, we investigate the performance of electricity theft detectors against evasion attacks that are designed to reduce the reported value of the energy consumption and at the same time fool the machine learning-based detector model via adversarial samples. Furthermore, we propose a strong evasion attack that significantly degrades the performance of a set of benchmark detectors. Our results reveal that evasion attacks can deteriorate the detection rate (DR) and false alarm (FA) rate by similar to 20%. To address such evasion attacks, we propose an ensemble learning-based detector that integrates auto-encoder with attention (AEA), long-shortterm-memory (LSTM), and feed forward deep neural networks. The developed detector maintains a stable detection performance against evasion attacks with a deterioration in performance by only 1 - 5% in DR and FA.