Evaluation of Data Preprocessing Techniques for Anomaly Detection Systems in Industrial Control System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F19%3A63523743" target="_blank" >RIV/70883521:28140/19:63523743 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2019/101.pdf" target="_blank" >https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2019/101.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2507/30th.daaam.proceedings.101" target="_blank" >10.2507/30th.daaam.proceedings.101</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of Data Preprocessing Techniques for Anomaly Detection Systems in Industrial Control System

Popis výsledku v původním jazyce

The critical infrastructure can be defined as main cornerstone of modern society. Therefore, the cyber protection of critical systems like industrial control systems is vital for every modern state. However, conventional techniques are often ineffective to protect these systems. Thus, machine learning is an exceptional way to ensure cyber security in the case of critical infrastructure. The machine learning can process high dimension datasets with thousands of record in real-time. However, these datasets have to be in a proper format. The data preprocessing is a crucial stage in machine learning and can negatively influence final results. We introduce a comprehensive comparison of the main data preprocessing techniques in the relation of the network anomaly detection system. Moreover, the preprocessing of continuous datasets is considered as the subject of the research The neural network autoencoder is considered as an anomaly detection algorithm which is used to evaluate proposed solutions.

Název v anglickém jazyce

Evaluation of Data Preprocessing Techniques for Anomaly Detection Systems in Industrial Control System

Popis výsledku anglicky

The critical infrastructure can be defined as main cornerstone of modern society. Therefore, the cyber protection of critical systems like industrial control systems is vital for every modern state. However, conventional techniques are often ineffective to protect these systems. Thus, machine learning is an exceptional way to ensure cyber security in the case of critical infrastructure. The machine learning can process high dimension datasets with thousands of record in real-time. However, these datasets have to be in a proper format. The data preprocessing is a crucial stage in machine learning and can negatively influence final results. We introduce a comprehensive comparison of the main data preprocessing techniques in the relation of the network anomaly detection system. Moreover, the preprocessing of continuous datasets is considered as the subject of the research The neural network autoencoder is considered as an anomaly detection algorithm which is used to evaluate proposed solutions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VI20172019054" target="_blank" >VI20172019054: Analytický programový modul pro hodnocení odolnosti v reálném čase z hlediska konvergované bezpečnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Annals of DAAAM and Proceedings of the International DAAAM Symposium

ISBN

—

ISSN

17269679

e-ISSN

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Počet stran výsledku

8

Strana od-do

738-745

Název nakladatele

Danube Adria Association for Automation and Manufacturing ( DAAAM )

Místo vydání

Vídeň

Místo konání akce

Zadar

Datum konání akce

23. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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