A Flexible and Scalable ML-Based Diagnosis Module for Optical Networks: A Security Use Case
Journal article, 2023

To support the pervasive digital evolution, optical network infrastructures must be able to quickly and effectively adapt to the changes arising from traffic dynamicity or external factors such as faults and attacks. Network automation is crucial for enabling dynamic, scalable, resource-efficient, and trustworthy network operations. Novel telemetry solutions enable optical network management systems to obtain fine-grained monitoring data from devices and channels as the first step towards the near-real-time diagnosis of anomalies such as security threats and soft failures. However, the collection of large amounts of data creates a scalability challenge related to processing the data within the desired monitoring cycle regardless of the number of optical services being analyzed. This paper proposes a module that leverages the cloud native software deployment approach to achieve near-real-time \ac{ML}-assisted diagnosis of optical channels. The results obtained over an emulated physical-layer security scenario demonstrate that the architecture successfully scales the necessary components according to the computational load, and consistently achieves the desired monitoring cycle duration over a varying number of monitored optical channels.

Author

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Lluis Gifre

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Francisco-Javier Moreno-Muro

Atos Spain

Sergio Gonzalez-Diaz

Atos Spain

Ricard Vilalta

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Raul Munoz

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)

Paolo Monti

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Journal of Optical Communications and Networking

1943-0620 (ISSN) 19430639 (eISSN)

Vol. 15 8 C155-C165

Safeguarding optical communication networks from cyber-security attacks

Swedish Research Council (VR) (2019-05008), 2020-01-01 -- 2023-12-31.

Secured autonomic traffic management for a Tera of SDN flows (TeraFlow)

European Commission (EC) (EC/H2020/101015857), 2021-01-01 -- 2023-06-30.

Subject Categories

Computer Engineering

Telecommunications

Computer Science

DOI

10.1364/JOCN.482932

More information

Latest update

7/19/2023