Effectiveness of Confidentiality-Preserving Clustering Algorithms for Soft Failure Detection in Optical Networks
Azarm Yeganehfallah, Andrea Sgambelluri, Alessandro Pacini, Luca Valcarenghi, Moises Felipe Mello da Silva · 2024
The implementation of zero-touch network and service management solutions in software defined optical networks requires the elaboration of detailed optical components’ information. However, such data can be elaborated by third parties. Thus, confidentiality issues may arise because providers are not willing to unveil their detailed information. This study proposes schemes based on dataset scrambling and unsupervised machine learning algorithms for soft failures detection in optical networks. A key aspect of the proposed scheme is the preservation of data confidentiality, that refers, in this context, to safeguard the detailed information of optical components while still enabling effective failure detection. The performance of six different clustering algorithms have been experimentally evaluated in a laboratory testbed. The results reveal that certain algorithms, while working in a confidentiality preserving scheme, perform very well in clustering different states (i.e., working and faulty states) of the network.