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Machine Learning for Optical Network Security Monitoring: A Practical Perspective

, , , , , and . Journal of Lightwave Technology, 38 (11): 2860--2871 (2020)
DOI: 10.1109/JLT.2020.2987032

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Experiment-based identification of service disruption attacks in optical networks, , , and . (February 2019)Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats Invited., , , and . JOCN, 13 (2): A144-A155 (2021)Flexible node architectures for metro networks., , and . OFC, page 1-3. IEEE, (2015)Field Demonstration of Machine-Learning-Aided Detection and Identification of Jamming Attacks in Optical Networks., , , , and . ECOC, page 1-3. IEEE, (2018)Economic viability of high bit rate and high spectral efficiency transponders., , and . ONDM, page 126-131. IEEE, (2014)Optical Node Disaggregation Management and Interoperability., and . OFC, page 1-3. IEEE, (2020)Machine Learning for Optical Network Security Monitoring: A Practical Perspective, , , , , and . Journal of Lightwave Technology, 38 (11): 2860--2871 (2020)Programmable multi-granular optical networks: requirements and architecture., , , , , , , , , and 1 other author(s). BROADNETS, page 1-7. IEEE, (2009)Flexible Node Architectures for Metro Networks Invited., , and . JOCN, 7 (12): B131-B140 (2015)Root Cause Analysis for Autonomous Optical Networks: A Physical Layer Security Use Case., , , and . ECOC, page 1-4. IEEE, (2020)