Semi-supervised Anomaly Detection with Imbalanced Data for Failure Detection in Optical Networks

We proposed an autoencoder-based anomaly-detection for optical failure detection with imbalanced data (<3%), which identifies implicit failure and achieves detection accuracy of 96.8% and F1 value of 0.9224.