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

, , , , and . ECOC, page 1-3. IEEE, (2018)

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Root Cause Analysis for Autonomous Optical Networks: A Physical Layer Security Use Case., , , and . ECOC, page 1-4. IEEE, (2020)Machine Learning for Optical Network Security Monitoring: A Practical Perspective, , , , , and . Journal of Lightwave Technology, 38 (11): 2860--2871 (2020)Optical Node Disaggregation Management and Interoperability., and . OFC, page 1-3. IEEE, (2020)Programmable multi-granular optical networks: requirements and architecture., , , , , , , , , and 1 other author(s). BROADNETS, page 1-7. IEEE, (2009)Experiment-based identification of service disruption attacks in optical networks, , , and . (February 2019)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)Root Cause Analysis for Autonomous Optical Network Security Management., , , and . IEEE Trans. Netw. Serv. Manag., 19 (3): 2702-2713 (2022)Access and Metro Network Convergence for Flexible End-to-End Network Design Invited., , , , , , , , , and 11 other author(s). JOCN, 9 (6): 524-535 (2017)