Comparative Study of Feature Selection Techniques Respecting Novelty Detection in the Industrial Control System Environment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63520130" target="_blank" >RIV/70883521:28140/18:63520130 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2018/155.pdf" target="_blank" >https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2018/155.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2507/29th.daaam.proceedings.155" target="_blank" >10.2507/29th.daaam.proceedings.155</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Study of Feature Selection Techniques Respecting Novelty Detection in the Industrial Control System Environment

Popis výsledku v původním jazyce

The emerging trend of interconnection between business processes and industrial processes resulted in a considerable number of cyber security incidents that show us how vulnerable Industrial Control Systems (ICS) are. These usually legacy systems were not designed with cyber security in mind. Therefore, there is a necessity for the reliable cyber security system. The anomaly detection based on machine learning techniques is the one potential way how to protect the system against cyber-attacks effectively. However, the ICS has become more sophisticated; therefore, produce high-dimensional datasets. Hence, the dimensionality reduction for the dataset is required due to high computational complexity. We introduce the comprehensive study on dimensionality reduction techniques which are applied to ICS network cyber security. Moreover, obtained results are evaluated by novelty detection algorithm where One-Class Support Vector Machine algorithm is used.

Název v anglickém jazyce

Comparative Study of Feature Selection Techniques Respecting Novelty Detection in the Industrial Control System Environment

Popis výsledku anglicky

The emerging trend of interconnection between business processes and industrial processes resulted in a considerable number of cyber security incidents that show us how vulnerable Industrial Control Systems (ICS) are. These usually legacy systems were not designed with cyber security in mind. Therefore, there is a necessity for the reliable cyber security system. The anomaly detection based on machine learning techniques is the one potential way how to protect the system against cyber-attacks effectively. However, the ICS has become more sophisticated; therefore, produce high-dimensional datasets. Hence, the dimensionality reduction for the dataset is required due to high computational complexity. We introduce the comprehensive study on dimensionality reduction techniques which are applied to ICS network cyber security. Moreover, obtained results are evaluated by novelty detection algorithm where One-Class Support Vector Machine algorithm is used.

Druh

D - Stať ve sborníku

CEP obor

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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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

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

978-3-902734-20-4

ISSN

1726-9679

e-ISSN

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Počet stran výsledku

8

Strana od-do

1084-1091

Název nakladatele

DAAAM International Vienna

Místo vydání

Vienna

Místo konání akce

Zadar

Datum konání akce

24. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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