用变分自编码器实现跨光系统威胁检测，显著提升模型泛化能力。
Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring
- 通过变分自编码器学习两系统共有的事件特征表示，抑制系统差异。
- 跨系统准确率分别达95.3%和73.5%，较基线提升83.4%和51%。
- 适合需要在不同光纤系统间部署ML监控的工业场景。
在一种光缆系统上训练的机器学习（ML）模型检测物理层威胁时，若应用于另一系统，常因工作波长、光纤特性与网络架构差异而失效。为此，本文提出基于变分自编码器（VAE）的域适应（DA）框架，学习共享表征以捕捉两系统共有的事件特征，同时抑制系统特异性差异。共享编码器首先在两个不同系统数据上联合训练：一个21公里的O波段暗光纤测试平台（系统1）和一个63.4公里的C波段实际城域环网（系统2）。编码器冻结后，仅用单个系统的标签训练分类器。该方法在从系统1到系统2及反向迁移时，跨系统准确率分别达到95.3%和73.5%，相较仅在一个系统上训练的全监督深度神经网络（DNN）基线，性能提升83.4%和51%，且保持了系统内性能。
原文摘要 · Abstract (English)
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.
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