Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151811" target="_blank" >RIV/00216305:26220/24:PU151811 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3664476.3670440" target="_blank" >https://dl.acm.org/doi/10.1145/3664476.3670440</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3664476.3670440" target="_blank" >10.1145/3664476.3670440</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks

Popis výsledku v původním jazyce

The Internet of Things (IoT) has become increasingly practical in applications such as smart homes, autonomous vehicles, and environmental monitoring. However, this rapid expansion has led to significant cybersecurity threats. Detecting these threats is critical, and while machine learning techniques are valuable, they struggle with high-dimensional data. Feature selection helps by reducing computational costs while maintaining model generalization. Selecting the most effective feature selection method is a crucial task. This research addresses this gap by testing five feature selection methods: Random Forest (RF), Recursive Feature Elimination (RFE), Logistic Regression (LR), XGBoost Regression (XGBoost), and Information Gain (IG) using the CIC-IoT 2023 dataset. It evaluates these methods when being used with five machine learning models: Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Gradient Boosting (GB), and Multi-layer Perceptron (MLP) using metrics like accuracy, precision, recall, and F1-score across three datasets. The results show that RFE, especially with the RF model, achieves the highest accuracy (99.57%) with 30 features. RF is the most stable, with accuracy from 83% to 99.56%. Additionally, the 5-feature scheme is best for implementing IDS on resource-limited IoT devices, with RFE paired with the k-NN model being the optimal combination.

Název v anglickém jazyce

Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks

Popis výsledku anglicky

The Internet of Things (IoT) has become increasingly practical in applications such as smart homes, autonomous vehicles, and environmental monitoring. However, this rapid expansion has led to significant cybersecurity threats. Detecting these threats is critical, and while machine learning techniques are valuable, they struggle with high-dimensional data. Feature selection helps by reducing computational costs while maintaining model generalization. Selecting the most effective feature selection method is a crucial task. This research addresses this gap by testing five feature selection methods: Random Forest (RF), Recursive Feature Elimination (RFE), Logistic Regression (LR), XGBoost Regression (XGBoost), and Information Gain (IG) using the CIC-IoT 2023 dataset. It evaluates these methods when being used with five machine learning models: Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Gradient Boosting (GB), and Multi-layer Perceptron (MLP) using metrics like accuracy, precision, recall, and F1-score across three datasets. The results show that RFE, especially with the RF model, achieves the highest accuracy (99.57%) with 30 features. RF is the most stable, with accuracy from 83% to 99.56%. Additionally, the 5-feature scheme is best for implementing IDS on resource-limited IoT devices, with RFE paired with the k-NN model being the optimal combination.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01030019" target="_blank" >VK01030019: Interaktivní kontrolní seznamy pro efektivní testování kybernetické bezpečnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

ISBN

979-8-4007-1718-5

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Association for Computing Machinery

Místo vydání

New York, NY, USA

Místo konání akce

Vídeň

Datum konání akce

30. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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