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 the performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing what we believe to be a novel functional block called the security operation center, 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 the ML accuracy and run-time complexity.

中文翻译：

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光网络安全管理：用于检测不断发展的威胁的需求，体系结构和高效的机器学习模型[已邀请]

作为维持关键社会服务的通信基础设施，光网络需要以安全和敏捷的方式运行。因此，需要认知和自动安全管理功能，这些功能由激增的机器学习（ML）技术推动，并与常见的网络控制实体和过程兼容。光网络安全性的自动化管理要求在用于安全诊断的ML方法的性能和效率以及新颖的管理体系结构和功能方面都有进步。本文通过提出我们认为是一种称为安全操作中心的新颖功能块，描述其体系结构，指定对所支持功能的关键要求，来应对这些挑战。并提供与光层控制器集成的指南。此外，为了在存在以前看不见的物理层攻击的情况下处理高维光学性能监控数据时提高基于ML的安全诊断技术的效率，我们将无监督和半监督学习技术与三种不同的降维方法相结合，并分析了从而在ML准确性和运行时复杂性之间取得了性能和取舍。