统一框架实现光网络零日异常检测与分类，无需重训

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

• 用多相似度孪生网络统一处理异常检测与分类
• 对未见异常类型准确率超99%，且即时适配新链路
• 适合需要快速响应未知攻击的光网络运维

一种多相似度孪生神经网络统一了光网络中的零日异常检测与单样本分类任务，在无需任何重训练的情况下，实现了超过99%的准确率，并能即时适应不同光通道及未见过的异常类型。

原文摘要 · Abstract (English)

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.

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