Root Cause Analysis for Autonomous Optical Network Security Management
نویسندگان
چکیده
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, diagnostic approaches should be able to detect and identify the nature not only existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known types, cannot guarantee precise identification attacks. This makes Root Cause Analysis (RCA) crucial enabling timely response human intervention is unavoidable. We address these challenges by establishing an ML-based framework assessment analyzing RCA alternatives physical-layer first scrutinize different Network Management System (NMS) architectures corresponding capabilities. then investigate applicability supervised unsupervised learning (SL UL) propose a novel UL-based algorithm called Distance-Based (DB-RCA). framework’s performance network management validated on experimental dataset, assessing benefits drawbacks SL- RCA. Besides confirming that SL-based can provide output types upon training, we show proposed approach offers meaningful insight into anomalies caused thus officers in advancing diagnostics.
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ژورنال
عنوان ژورنال: IEEE Transactions on Network and Service Management
سال: 2022
ISSN: ['2373-7379', '1932-4537']
DOI: https://doi.org/10.1109/tnsm.2022.3198139