GPON ATTACKS AND ERRORS CLASSIFICATION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141219" target="_blank" >RIV/00216305:26220/21:PU141219 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf" target="_blank" >https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_1.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

GPON ATTACKS AND ERRORS CLASSIFICATION

Popis výsledku v původním jazyce

This paper focuses on various types of attacks and errors in an activation process of Gigabit-capable passive optical networks. The process sends messages via Physical Layer Operation Administration and Maintenance header field inside the transmitted frame. An exemplar network communication is captured by a special hardware-accelerated network interface card capable of processing optical signals from passive optical networks. The captured data is filtered of irrelevant parts and messages and correctly formatted into a suitable shape for a neural network. The filtered data is divided into small sequences called time windows and analyzed using a recurrent neural network-based on Gated recurrent 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 distinguish message sequences in

Název v anglickém jazyce

GPON ATTACKS AND ERRORS CLASSIFICATION

Popis výsledku anglicky

This paper focuses on various types of attacks and errors in an activation process of Gigabit-capable passive optical networks. The process sends messages via Physical Layer Operation Administration and Maintenance header field inside the transmitted frame. An exemplar network communication is captured by a special hardware-accelerated network interface card capable of processing optical signals from passive optical networks. The captured data is filtered of irrelevant parts and messages and correctly formatted into a suitable shape for a neural network. The filtered data is divided into small sequences called time windows and analyzed using a recurrent neural network-based on Gated recurrent 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 distinguish message sequences in

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VI20192022135" target="_blank" >VI20192022135: Hloubková hardwarová detekce síťového provozu pasivních optických sítí nové generace v kritických infrastrukturách</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů