A hybrid lightweight system for early attack detection in the IoMT fog

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018731" target="_blank" >RIV/62690094:18450/21:50018731 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/21/24/8289" target="_blank" >https://www.mdpi.com/1424-8220/21/24/8289</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21248289" target="_blank" >10.3390/s21248289</a>

Result language

angličtina

Original language name

A hybrid lightweight system for early attack detection in the IoMT fog

Original language description

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT’s big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

—

Volume of the periodical

21

Issue of the periodical within the volume

24

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

"Article number: 8289"

UT code for WoS article

000737474800001

EID of the result in the Scopus database

2-s2.0-85121439895