Identifying Industry Devices via Time Delay in Dataflow

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU150132" target="_blank" >RIV/00216305:26220/23:PU150132 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3638782.3638808" target="_blank" >http://dx.doi.org/10.1145/3638782.3638808</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3638782.3638808" target="_blank" >10.1145/3638782.3638808</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identifying Industry Devices via Time Delay in Dataflow

Popis výsledku v původním jazyce

In networks with critical industrial processes where operational integrity is paramount, device identification is crucial for security and effective management. Without such identification, the potential for mismanagement and security breaches increases. Active scanning for network device identification poses risks, especially in industrial settings. Such scanning can disrupt operations or even cause damage. Therefore, finding non-invasive identification methods that bypass active scanning is imperative. Passive scanning, owing to its non-intrusive approach, is favored for industrial devices. Modern statistical learning techniques combined with passive scanning can mitigate risks of active methods. Our research harnesses time delay data in network communications to accurately identify specific industrial PLC models. We derive our data from timestamp details of the OPC UA protocol, widely recognized as a standard in industrial communication. Statistical variables from time delay data enhance the accuracy of passive device identification in industrial settings.

Název v anglickém jazyce

Identifying Industry Devices via Time Delay in Dataflow

Popis výsledku anglicky

In networks with critical industrial processes where operational integrity is paramount, device identification is crucial for security and effective management. Without such identification, the potential for mismanagement and security breaches increases. Active scanning for network device identification poses risks, especially in industrial settings. Such scanning can disrupt operations or even cause damage. Therefore, finding non-invasive identification methods that bypass active scanning is imperative. Passive scanning, owing to its non-intrusive approach, is favored for industrial devices. Modern statistical learning techniques combined with passive scanning can mitigate risks of active methods. Our research harnesses time delay data in network communications to accurately identify specific industrial PLC models. We derive our data from timestamp details of the OPC UA protocol, widely recognized as a standard in industrial communication. Statistical variables from time delay data enhance the accuracy of passive device identification in industrial settings.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01030109" target="_blank" >VK01030109: Výzkum holistického modelu propojené kritické elektroenergetické a komunikační infrastruktury</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

ICCNS 2023 Proceedings

ISBN

979-8-4007-0796-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

neuveden

Místo konání akce

Fuzhou, China

Datum konání akce

1. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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