Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151320" target="_blank" >RIV/00216305:26220/24:PU151320 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1999-4893/17/5/208" target="_blank" >https://www.mdpi.com/1999-4893/17/5/208</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/a17050208" target="_blank" >10.3390/a17050208</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

Popis výsledku v původním jazyce

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.

Název v anglickém jazyce

Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

Popis výsledku anglicky

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/FW07010004" target="_blank" >FW07010004: Využití předností sítí páté generace pro monitorování, optimalizaci a zefektivnění výrobního procesu v chytrých továrnách</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Algorithms

ISSN

1999-4893

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001232223700001

EID výsledku v databázi Scopus

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