ML-based Anomaly Detection in Optical Fiber Monitoring

• URL: http://arxiv.org/abs/2202.11756v1
• Date: Wed, 23 Feb 2022 19:43:37 GMT
• Title: ML-based Anomaly Detection in Optical Fiber Monitoring
• Authors: Khouloud Abdelli, Joo Yeon Cho, Carsten Tropschug
• Abstract summary: We propose a data driven approach for anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber breaks and optical tapping. We verify the efficiency of our methods by experiments under various attack scenarios using real operational data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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