Root Cause Analysis for Autonomous Optical Network Security Management

The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the na...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2022-09, Vol.19 (3), p.2702-2713
Main Authors: Natalino, Carlos, Schiano, Marco, Giglio, Andrea Di, Furdek, Marija
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
explainability
interpretability
Jamming
Machine learning
Monitoring
Network security
Optical communication
Optical fiber networks
Optical fibers
Optical networks
Optical polarization
physical layer security
Root cause analysis
Security
Security management
supervised learning
Telemetry
unsupervised learning
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Summary:The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework's applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL- and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2022.3198139