论文标题
光纤监测中基于ML的基于ML的异常检测
ML-based Anomaly Detection in Optical Fiber Monitoring
论文作者
论文摘要
光网络中的安全可靠数据通信对于高速互联网至关重要。我们提出了一种数据驱动的方法，用于在光网络中进行异常检测和故障识别，以诊断物理攻击，例如纤维断裂和光学攻击。所提出的方法包括基于自动编码器的异常检测和用于纤维断层识别和定位的基于注意力的双向封盖复发单位算法。我们使用实际操作数据在各种攻击方案下通过实验来验证方法的效率。
Secure and reliable data communication in optical networks is critical for high-speed internet. We propose a data driven approach for the anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber breaks and optical tapping. The proposed methods include an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm for the fiber fault identification and localization. We verify the efficiency of our methods by experiments under various attack scenarios using real operational data.
