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dc.contributor.authorTomašov, Adrián
dc.date.accessioned2021-07-21T07:06:59Z
dc.date.available2021-07-21T07:06:59Z
dc.date.issued2021cs
dc.identifier.citationProceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 332-336. ISBN 978-80-214-5942-7cs
dc.identifier.isbn978-80-214-5942-7
dc.identifier.urihttp://hdl.handle.net/11012/200774
dc.description.abstractThis paper focuses on various types of attacks and errors in an activation process of Gigabitcapablepassive optical networks. The process sends messages via Physical Layer Operation Administrationand Maintenance header field inside the transmitted frame. An exemplar network communicationis captured by a special hardware-accelerated network interface card capable of processing opticalsignals from passive optical networks. The captured data is filtered of irrelevant parts and messagesand correctly formatted into a suitable shape for a neural network. The filtered data is divided intosmall sequences called time windows and analyzed using a recurrent neural network-based on Gatedrecurrent unit cells. A new neural network model is designed to classify sequences into several categories:additional message, missing message, error inside (noisy) message, and message order error.All of these categories represent a certain type of attack or error. The proposed model can distinguishmessage sequences into these categories with high accuracy resulting in revealing a possible attackeror drift from protocol recommendation.en
dc.formattextcs
dc.format.extent332-336cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 27st Conference STUDENT EEICT 2021: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectActivation Processen
dc.subjectGPONen
dc.subjectGRUen
dc.subjectRecurrent Neural Networken
dc.subjectPLOAMen
dc.titleGpon Attacks And Errors Classificationen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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