LAUSR

Existing machine learning-based detectors of electricity theft cyberattacks are trained to detect only simple traditional types of cyberattacks while neglecting complex ones like evasion attacks. This paper analyzes the robustness of electricity theft detectors against evasion attacks. Such attacks decrease the reported electricity reading values and fool the electricity theft detectors by injecting adversarial samples. We propose strong evasion attacks that fool the benchmark detectors by iteratively generating adversarial samples based on an electricity reading and its neighboring readings. We study the impact of evasion attacks using white, gray, and black-box settings based on the attacker’s knowledge about the detector’s parameters or datasets. Our investigations revealed that the performance degradation of benchmark detectors is up to 35.8%, 26.9%, and 22.2% in white, gray, and black-box settings, respectively. To enhance the detection robustness, we propose an ensemble learning-based anomaly detector trained only on benign data to detect unseen attacks (traditional and evasion) by sequentially combining an attentive autoencoder, convolutional-recurrent, and feed forward neural networks. The proposed model offers a stable detection performance where the average degradation is only $0.7 - 3\%$ , $0.9 - 2.1\%$ , and $0.4 - 1.7\%$ in white, gray, and black-box settings, respectively, with maximum adversarial sample injection levels.