Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited]
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| Titel: | Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited] |
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| Autoren: | Furdek Prekratic, Marija, 1985, Natalino Da Silva, Carlos, 1987, Giglio, Andrea Di, Schiano, Marco |
| Quelle: | Skydda optiska kommunikationsnätverk från cyber-säkerhetsattacker Journal of Optical Communications and Networking. 13(2):A144-A155 |
| Schlagwörter: | Efficiency, Economic and social effects, Learning systems, Dimensionality reduction, Fiber optic networks, Network architecture, Semi-supervised learning, Infrastructure as a service (IaaS), Optical communication |
| Beschreibung: | As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/523192 https://research.chalmers.se/publication/519924 https://research.chalmers.se/publication/522115 https://research.chalmers.se/publication/523192/file/523192_Fulltext.pdf |
| Datenbank: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity. |
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| ISSN: | 19430620 19430639 |
| DOI: | 10.1364/JOCN.402884 |