Field Demonstration of Machine-Learning-Aided Detection and Identification of Jamming Attacks in Optical Networks

We develop a machine-learning-aided framework for detection and identification of optical network jamming signal attacks of varying intensities. Trained with data gathered in our field-deployed experimental setup, the approach achieves 93% accuracy on average over the considered attack scenarios.

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