查看文章

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This article focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL), and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false …