Machine-learning-based anomaly detection in optical fiber monitoring

• Published in: Journal of optical communications and networking Vol. 14; no. 5; pp. 365 - 375
• Main Authors: Abdelli, Khouloud, Cho, Joo Yeon, Azendorf, Florian, Griesser, Helmut, Tropschug, Carsten, Pachnicke, Stephan
• Format: Journal Article
• Language: English
• Published: Piscataway Optica Publishing Group 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Anomaly detection; Data communication; Data loss; Data transmission; Eavesdropping; Fault detection; Fault diagnosis; Localization; Logic gates; Machine learning; Network reliability; Optical communication; Optical fiber cables; Optical fibers; Optical network units; Optical pulses
• ISSN: 1943-0620; 1943-0639
• DOI: 10.1364/JOCN.451289

Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physical attacks [e.g., optical eavesdropping (fiber tapping)]. Such anomalies may cause network disruption, thereby inducing huge financial and data losses, compromising the confidentiality of optical networks by gaining unauthorized access to the carried data, or gradually degrading the network operations. Therefore, it is highly required to implement efficient anomaly detection, diagnosis, and localization schemes for enhancing the availability and reliability of optical networks. In this paper, we propose a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder. We verify the efficiency of our proposed approach by experiments under various attack anomaly scenarios using real operational data. The experimental results demonstrate that (i) the autoencoder detects any fiber fault or anomaly with an F1 score of 96.86%, and (ii) the attention-based bidirectional gated recurrent unit algorithm identifies the detected anomalies with an average accuracy of 98.2% and localizes the faults with an average root mean square error of 0.19 m.