Machine-learning-based method for fiber-bending eavesdropping detection

Article Engineering, Electrical & Electronic

Experimental study of machine-learning-based detection and location of eavesdropping in end-to-end optical fiber communications

Haokun Song et al.

Summary: This study proposes a Machine Learning-based scheme to detect and locate physical-layer eavesdropping in optical fiber communications, achieving high accuracy through the use of Optical Performance Monitoring data and eye-diagrams as input. Experiments conducted in a fiber transmission system show promising results with ML classifiers achieving 100% and 92.76% accuracy in eavesdropping detection and location, respectively.

OPTICAL FIBER TECHNOLOGY (2022)

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Article Computer Science, Hardware & Architecture

Machine-learning-based anomaly detection in optical fiber monitoring

Khouloud Abdelli et al.

Summary: This paper proposes a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks in optical networks. The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm. Experimental results show the effectiveness of the approach.

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING (2022)

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Article Computer Science, Hardware & Architecture

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

Marija Furdek et al.

Summary: This paper proposes a novel functional block called the security operation center for automated security management in optical networks. Additionally, the study combines various learning techniques and dimensionality reduction methods to enhance the efficiency of processing high-dimensional optical performance monitoring data.

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING (2021)

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Article Engineering, Electrical & Electronic

An Interferometric Optical Fiber Perimeter Security System Based on Multi-Domain Feature Fusion and SVM

Jiabin Shi et al.

Summary: This paper reports an interferometric optical fiber perimeter security system based on multi-domain feature fusion and support vector machine (SVM), which improves intrusion event classification accuracy and reduces overall cost. The system combines advanced optical fiber sensor technique and data analysis method, achieving an average classification accuracy of 94.4% in field experiments.

IEEE SENSORS JOURNAL (2021)

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Article Computer Science, Hardware & Architecture

Monitoring and diagnostic technologies using deep neural networks for predictive optical network maintenance [Invited]

Takafumi Tanaka et al.

Summary: In recent years, optical networks have become more complex, requiring autonomous optical network diagnosis and the support of machine learning. Another trend is to reduce capital expenditure/operational expenditure of optical transport equipment through whitebox hardware, open source software, and open interfaces. The paper describes in detail the concept of workflows for the whitebox transponder and demonstrates successful fiber bending detection as a use case.

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING (2021)

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Article Engineering, Electrical & Electronic