A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks

A multi-similarity Siamese neural network framework has been proposed for zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy. This model demonstrates instant adaptability to new lightpaths and previously unseen anomaly types without requiring retraining. This is significant for practitioners as it enables real-time anomaly detection in dynamic optical environments, reducing downtime and operational costs.