On Real-Time and Self-Taught Anomaly Detection in Optical Networks Using Hybrid Unsupervised/Supervised Learning

Abstract

This paper proposes a real-time and self-taught anomaly detection scheme for optical networks using hybrid unsupervised/supervised learning. Evaluations with an experimental dataset demonstrate that the proposed scheme can successfully identify 100% of the anomalies without any prior knowledge of abnormal network behaviors while restricting the false positive rate to be only 6.5%.