Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids

Data-driven electricity theft detectors rely on customers’ reported energy consumption readings to detect malicious behavior. One common implicit assumption in such detectors is the correct labeling of the training data. Unfortunately, these detectors are vulnerable against data poisoning attacks that assume false labels during training. This article addresses three major problems: What is the imp...