Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/VK01030019" target="_blank" >VK01030019: Interactive checklists for effective cybersecurity testing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security
ISBN
979-8-4007-1718-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, USA
Event location
Vídeň
Event date
Jul 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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