Machine Learning-Based Attack Detection for the Internet of Things

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39922510" target="_blank" >RIV/00216275:25410/25:39922510 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.future.2024.107630" target="_blank" >https://doi.org/10.1016/j.future.2024.107630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2024.107630" target="_blank" >10.1016/j.future.2024.107630</a>

Result language

angličtina

Original language name

Machine Learning-Based Attack Detection for the Internet of Things

Original language description

The number of Internet of Things (IoT) device connections is increasing rapidly as IoT applications are vital in any operation. IoT must maintain safe internet access that withstands various malicious attacks for instance Recon, Mirai, Distributed Denial of Service (DDoS), and Spoofing which has gained much attention. Intelligently changing and zero-day attacks are emerging every day. This highlights the need for intelligent security solutions tailored specifically to this technology. Various Machine Learning (ML) based approaches have been utilized for intrusion detection to tackle IoT attacks. However, the flaws of current attack detection and feature extraction techniques result in low detection accuracy. Thus, it hindered their real-world applications and highlighted the need fora lightweight and computationally robust model trained and assessed on a recent datasets. Therefore, this work proposed an attack detection model trained and validated using the CICIoT2023 and CICIDS2017 datasets. Initially, data preprocessing is done then features are extracted by using an unsupervised Elastic Deep Autoencoder (EDA) with optimum hyperparameters. Further, the Extreme Gradient Boosting (XGBoost) binary classifier is tuned by the Grey Wolf Optimizer (GWO) and fed extracted feature sets to classify attacks. The results of the experiments show the effectiveness of our model with a higher detection accuracy in both datasets. Finally, the performance comparison confirmed that the results of the proposed work is competitive with other state-of-the-art method in securing IoT infrastructures.

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

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

1872-7115

Volume of the periodical

166

Issue of the periodical within the volume

May

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

107630

UT code for WoS article

001374118800001

EID of the result in the Scopus database

2-s2.0-85211078881