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Field Demonstration of Machine-Learning-Aided Detection and Identification of Jamming Attacks in Optical Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Optical Network Laboratory (ON Lab).ORCID iD: 0000-0001-7501-5547
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Optical Network Laboratory (ON Lab).ORCID iD: 0000-0001-6704-6554
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2018 (English)In: European Conference on Optical Communication, ECOC, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

We develop a machine-learning-aided framework for detection and identification of optical network jamming signal attacks of varying intensities. Trained with data gathered in our field-deployed experimental setup, the approach achieves 93% accuracy on average over the considered attack scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
Fiber optic networks, Jamming, Machine learning, Attack scenarios, Detection and identifications, Jamming attacks, Jamming signals, Optical communication
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-252253DOI: 10.1109/ECOC.2018.8535155Scopus ID: 2-s2.0-85063210375ISBN: 9781538648629 (print)OAI: oai:DiVA.org:kth-252253DiVA, id: diva2:1324895
Conference
2018 European Conference on Optical Communication, ECOC 2018, 23 September 2018 through 27 September 2018
Note

QC20190614

Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-14Bibliographically approved

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Publisher's full textScopushttps://www.ecoc2018.org/

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Natalino, CarlosWosinska, LenaFurdek, Marija
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