A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques

IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, m...

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Главные авторы:	Lapina, M. A., Лапина, М. А.
Формат:	Статья
Язык:	English
Опубликовано:	Institute of Electrical and Electronics Engineers Inc. 2024
Темы:	Botnet attacks Machine Learning Feature selection IoT security
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29212
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record_format	dspace
spelling	ir-123456789-292122024-11-08T12:26:45Z A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques Lapina, M. A. Лапина, М. А. Botnet attacks Machine Learning Feature selection IoT security IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers. 2024-11-08T12:26:06Z 2024-11-08T12:26:06Z 2024 Статья Fernando C.A., Thomas R., Mary Anita E.A., Lapina M. A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques // Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications. - 2024. - DOI: 10.1109/InC460750.2024.10649035 https://dspace.ncfu.ru/handle/123456789/29212 en Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications application/pdf Institute of Electrical and Electronics Engineers Inc.
institution	СКФУ
collection	Репозиторий
language	English
topic	Botnet attacks Machine Learning Feature selection IoT security
spellingShingle	Botnet attacks Machine Learning Feature selection IoT security Lapina, M. A. Лапина, М. А. A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
description	IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers.
format	Статья
author	Lapina, M. A. Лапина, М. А.
author_facet	Lapina, M. A. Лапина, М. А.
author_sort	Lapina, M. A.
title	A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
title_short	A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
title_full	A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
title_fullStr	A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
title_full_unstemmed	A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
title_sort	feature selection study on the bot-iot dataset using ensemble classification techniques
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29212
work_keys_str_mv	AT lapinama afeatureselectionstudyonthebotiotdatasetusingensembleclassificationtechniques AT lapinama afeatureselectionstudyonthebotiotdatasetusingensembleclassificationtechniques AT lapinama featureselectionstudyonthebotiotdatasetusingensembleclassificationtechniques AT lapinama featureselectionstudyonthebotiotdatasetusingensembleclassificationtechniques
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A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques

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