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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Article name in the collection

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

—

Number of pages

12

Pages from-to

485-496

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Berlín

Event location

Kitakyushu

Event date

Jul 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000876774100041