Artificial immunity-based energy theft detection for advanced metering infrastructures

Abstract

Advanced Metering Infrastructure (AMI) is envisioned to enable smart energy management and consumption while ensuring the integrity of real energy consumption data. However, existing smart meters, gateways, and communication channels are usually weakly protected, often opening a huge door for data eavesdroppers who may be easily to further construct energy thefts. Although some energy theft detection schemes have already been reported in the literature, they often fail to take into account the dense data distribution characteristics of energy consumption data, resulting in compromised detection performance. To this end, we in this paper propose a novel arTificial IMmune based Energy theft Detection (TIMED) scheme, which can effectively identify five types of energy thefts. Specifically, we first develop an energy consumption data pre-processing method, which can effectively reduce the dimensionality of raw energy consumption data to facilitate the data analyzing efficiency. Second, we design a center-distance-based energy theft detector generation method to create high-quality detectors with low elimination rates. Last, we devise a nonself-based hole repair method for energy theft detectors, which can further reduce the false negative alarms. Extensive experiments on a real public AMI dataset demonstrate that the proposed TIMED scheme is highly effective in identifying pulse attacks, scaling attacks, ramping attacks, random attacks, and smooth-curve attacks. The results show that TIMED outperforms many existing machine learning and traditional artificial immunity-based energy theft detection methods.