Learning to Match Prototype for Few-Shot Classification of Attacks and Faults in Smart Grids

The rapid deployment of advanced metering infrastructure facilitates the development of data-driven attack detection methods in smart grids, which typically rely on large amounts of labeled data for training. However, when new types of attacks or faults emerge, security analysts may only intercept limited malicious samples. The scarcity of samples makes it difficult for data-driven methods to learn effective decision boundaries, leading to degraded detection performance. In this work, we bridge the gap by learning class prototype representations from limited samples and learning to match unlabeled samples with corresponding prototypes. Specifically, we propose a metalearning-based framework termed Learning to Match Prototype (L2MP), which consists of a prototypical network (ProtoNet) that learns prototype representations by aggregating features from labeled samples, and a matching network that assesses the matching degree between unlabeled samples and prototypes for classification. Through episodic training designed to simulate the few-shot setting, L2MP learns to adapt to novel attack and fault types with only a few samples per class. Moreover, we utilize a bilevel optimization strategy to ensure efficient training of both networks. Extensive case studies on smart grid datasets demonstrate that L2MP achieves robust performance under harsh learning conditions and has practical utility in real-world scenarios.