WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019441" target="_blank" >RIV/62690094:18450/22:50019441 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-08530-7_41" target="_blank" >http://dx.doi.org/10.1007/978-3-031-08530-7_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08530-7_41" target="_blank" >10.1007/978-3-031-08530-7_41</a>

Jazyk výsledku

angličtina

Název v původním jazyce

WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT

Popis výsledku v původním jazyce

The fog-based attack detection systems can surpass cloud-based detection models due to their fast response and closeness to IoT devices. However, current fog-based detection systems are not lightweight to be compatible with ever-increasing IoMT big data and fog devices. To this end, a lightweight fog-based attack detection system is proposed in this study. Initially, a fog-based architecture is proposed for an IoMT system. Then the detection system is proposed which uses incremental ensemble learning, namely Weighted Hoeffding Tree Ensemble (WHTE), to detect multiple attacks in the network traffic of industrial IoMT system. The proposed model is compared to six incremental learning classifiers. Results of binary and multi-class classifications showed that the proposed system is lightweight enough to be used for the edge and fog devices in the IoMT system. The ensemble WHTE took trade-off between high accuracy and low complexity while maintained a high accuracy, low CPU time, and low memory usage. © 2022, Springer Nature Switzerland AG.

Název v anglickém jazyce

WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT

Popis výsledku anglicky

The fog-based attack detection systems can surpass cloud-based detection models due to their fast response and closeness to IoT devices. However, current fog-based detection systems are not lightweight to be compatible with ever-increasing IoMT big data and fog devices. To this end, a lightweight fog-based attack detection system is proposed in this study. Initially, a fog-based architecture is proposed for an IoMT system. Then the detection system is proposed which uses incremental ensemble learning, namely Weighted Hoeffding Tree Ensemble (WHTE), to detect multiple attacks in the network traffic of industrial IoMT system. The proposed model is compared to six incremental learning classifiers. Results of binary and multi-class classifications showed that the proposed system is lightweight enough to be used for the edge and fog devices in the IoMT system. The ensemble WHTE took trade-off between high accuracy and low complexity while maintained a high accuracy, low CPU time, and low memory usage. © 2022, Springer Nature Switzerland AG.

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-031-08529-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

485-496

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Kitakyushu

Datum konání akce

19. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000876774100041