Machine Learning-Based Attack Detection for the Internet of Things

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Based Attack Detection for the Internet of Things

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning-Based Attack Detection for the Internet of Things

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

1872-7115

Svazek periodika

166

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

107630

Kód UT WoS článku

001374118800001

EID výsledku v databázi Scopus

2-s2.0-85211078881