Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring

Leyla Sadighi; Stefan Karlsson; Carlos Natalino Da Silva; Mojtaba Eshghie; Fehmida Usmani; Eoin Kenny; Lena Wosinska; Paolo Monti; Marco Ruffini; Marija Furdek Prekratic

Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring
Artikel i vetenskaplig tidskrift, 2026

Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations in operating wavelength, fiber properties, and network architecture. To overcome this, we propose a Domain Adaptation (DA) framework based on a Variational Autoencoder (VAE) that learns a shared representation capturing event signatures common to both systems while suppressing system-specific differences. The shared encoder is first trained on the combined data from two distinct optical systems: a 21~km O-band dark-fiber testbed (System~1) and a 63.4~km C-band live metro ring (System~2). The encoder is then frozen, and a classifier is trained using labels from an individual system. The proposed approach achieves 95.3\% and 73.5\% cross-system accuracy when moving from System~1 to System~2 and vice versa, respectively. This corresponds to gains of 83.4\% and 51\% over a fully supervised Deep Neural Network (DNN) baseline trained on a single system, while preserving intra-system performance.