Machine Learning-based Anomaly Detection in Optical Fiber Monitoring

TL;DR

This paper presents a data-driven machine learning approach combining autoencoders and attention-based RNNs for accurate, rapid detection, diagnosis, and localization of fiber anomalies in optical networks, enhancing their security and reliability.

Contribution

The paper introduces a novel hybrid ML method integrating autoencoders and attention-based RNNs for optical fiber anomaly detection and localization, verified with real operational data.

Findings

Autoencoder detects fiber anomalies with 96.86% F1 score.

Attention-based RNN identifies anomalies with 98.2% accuracy.

Faults are localized with an average error of 0.19 meters.

Abstract

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)) etc. Such anomalies may cause network disruption and thereby inducing huge financial and data losses, or compromise the confidentiality of optical networks by gaining unauthorized access to the carried data, or gradually degrade 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 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 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 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.