Optical network modelling-based data analytics for network monitoring and security analysis using hybrid computing models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.1007/s11082-023-05527-9.pdf
Reference21 articles.
1. Abdelli, K., Cho, J.Y., Azendorf, F., Griesser, H., Tropschug, C., Pachnicke, S.: Machine-learning-based anomaly detection in optical fiber monitoring. J Opt Commun Netw 14(5), 365–375 (2022a)
2. Abdelli, K., Grießer, H., Pachnicke, S.: A machine learning-based framework for predictive maintenance of semiconductor laser for optical communication. J. Lightwave Technol. 40(14), 4698–4708 (2022b)
3. Abdelli, K., Tropschug, C., Griesser, H., Pachnicke, S.: Faulty branch identification in passive optical networks using machine learning. J Opt Commun Netw 15(4), 187–196 (2023)
4. Chen, X., Liu, C.Y., Proietti, R., Li, Z., Yoo, S.B.: Automating optical network fault management with machine learning. IEEE Commun. Mag. 60(12), 88–94 (2022)
5. Cho, J. Y., Pedreno-Manresa, J. J., Patri, S., Abdelli, K., Tropschug, C., Zou, J., Rydlichowski, P.: DeepALM: holistic optical network monitoring based on machine learning. In: 2022 Optical Fiber Communications Conference and Exhibition (OFC) (pp. 1–3). IEEE. (2022)
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