Comparative Study of Feature Selection Techniques Respecting Novelty Detection in the Industrial Control System Environment
Identifikátory výsledku
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>
Alternativní jazyky
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.
Klasifikace
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)
Návaznosti výsledku
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
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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
—
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
—