Systems and methods for few-shot network anomaly detection via cross-network meta-learning

Abstract

Various embodiments for few-shot network anomaly detection via cross-network meta-learning are disclosed herein. An anomaly detection system incorporating a new family of graph neural networks—Graph Deviation Networks (GDN) can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network. Further, the GDN is equipped with a new cross-network meta-learning algorithm (Meta-GDN) to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Extensive evaluations demonstrate the efficacy of the anomaly detection system and the Meta-GDN on few-shot or even one-shot network anomaly detection.