Electricity Theft Detection for Smart Homes with Knowledge-Based Synthetic Attack Data

Abstract

Electricity thefts are conventionally manually detected by inspections, accusations, and the failure of meters. However, the recent evolution of machine learning may allow the automatic detection of electricity theft only from the patterns of meter readings. Electric consumption heavily relies on many factors, e.g., the lifestyle of the day and the weather, and thus the accuracy of detection is questioned. We propose an electricity theft detection framework for smart homes with knowledge-based synthetic attack data. This allows training of the attack classifier only from the legitimate power consumption data, i.e, without attack actions and associated labels. We identified five attack patterns as the knowledge which consisted of smart attacks and legacy attacks. We have conducted comprehensive evaluations with nine machine learning models using the Almanac of Minutely Power dataset version 2 (AMPds2) dataset fine-grained time-series data of a smart home. We found that Gradient Boosting-based algorithms achieved the best, and Random Forest performed alternatively with almost 100% accuracy for detecting and classifying legacy attacks. Some smart attacks were not detected, but those algorithms achieved good performance in detection and classification.