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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.18035 |
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| _version_ | 1866911607912660992 |
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| author | Sadighi, Leyla Karlsson, Stefan Natalino, Carlos Eshghie, Mojtaba Usmani, Fehmida Kenny, Eoin Wosinska, Lena Monti, Paolo Furdek, Marija Ruffini, Marco |
| author_facet | Sadighi, Leyla Karlsson, Stefan Natalino, Carlos Eshghie, Mojtaba Usmani, Fehmida Kenny, Eoin Wosinska, Lena Monti, Paolo Furdek, Marija Ruffini, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring Sadighi, Leyla Karlsson, Stefan Natalino, Carlos Eshghie, Mojtaba Usmani, Fehmida Kenny, Eoin Wosinska, Lena Monti, Paolo Furdek, Marija Ruffini, Marco Machine Learning 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. |
| title | Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.18035 |