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

arXiv: https://arxiv.org/abs/2606.10827 · HTML: https://mlsanity.com/paper/2606.10827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 1 of 1)

- [Machine Learning-based Anomaly Detection in Optical Fiber Monitoring](https://mlsanity.com/paper/2204.07059/) (2022)

## Built on by (most cited first, 2 of 2)

- [Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning](https://mlsanity.com/paper/2607.20666/) (2026)
- [Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features](https://mlsanity.com/paper/2608.00044/) (2026)

## Abstract (arXiv)

A multi-similarity Siamese neural network unifies zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy and instant adaptability across lightpaths and unseen anomaly types without any retraining.