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Improvement of attack detection performance on the internet of things with PSO-search and random forest

The presence of the internet of things allows various smart devices to be connected and interact with each other. Although IoT provides benefits in daily activities, however, with the presence of new technologies, IoT is vulnerable to new types of attacks. The massive IoT traffic results in a large...

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Main Authors:	Kurniabudi, Kurniabudi, Deris, Stiawan, Darmawijoyo, Darmawijoyo, Idris, Mohd. Yazid, Defit, Sarjon, Triana, Yaya Sudarya, Budiarto, Rahmat
Format:	Article
Language:	English
Published:	Elsevier Ltd. 2022
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/103047/1/MohdYazidIdris2022_ImprovementofAttackDetectionPerformance.pdf http://eprints.utm.my/103047/ https://dx.doi.org/10.1016/j.jocs.2022.101833
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spelling	my.utm.1030472023-10-12T09:09:03Z http://eprints.utm.my/103047/ Improvement of attack detection performance on the internet of things with PSO-search and random forest Kurniabudi, Kurniabudi Deris, Stiawan Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Defit, Sarjon Triana, Yaya Sudarya Budiarto, Rahmat QA75 Electronic computers. Computer science The presence of the internet of things allows various smart devices to be connected and interact with each other. Although IoT provides benefits in daily activities, however, with the presence of new technologies, IoT is vulnerable to new types of attacks. The massive IoT traffic results in a large number of traffic features and constructs complex network that makes intrusion detection systems (IDSs) require large resources to identify the type of attacks. On the other hand, most of the intrusion detection techniques are not feasible for IoT networks because they require more computing resources for attack detection, while IoT devices have limited computing resources and storage capacity. Thus, a lightweight IDS that has ability to identify new types of attacks is required. This research proposes a hybrid of Panigrahi and PSO-Search approaches to reduce the complexity of the network by eliminating the number of irrelevant features effectively and efficiently and combine with Random Forest optimization method to improve detection performance. The proposed IDS is validated with training and testing data, using hold-out, Stratified k-fold cross-validation, and percentage split test mode on CICIDS-2017 dataset MachineLearningCSV version. The dataset is chosen, as it represents real IoT network traffic data. Experimental results show that the performance improvement of the proposed hybrid IDS is very encouraging. The accuracy rate reaches 99.9 %, with an average Recall value of 1.000. Elsevier Ltd. 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/103047/1/MohdYazidIdris2022_ImprovementofAttackDetectionPerformance.pdf Kurniabudi, Kurniabudi and Deris, Stiawan and Darmawijoyo, Darmawijoyo and Idris, Mohd. Yazid and Defit, Sarjon and Triana, Yaya Sudarya and Budiarto, Rahmat (2022) Improvement of attack detection performance on the internet of things with PSO-search and random forest. Journal of Computational Science, 64 (101833). pp. 1-13. ISSN 1877-7503 https://dx.doi.org/10.1016/j.jocs.2022.101833 DOI: https://doi.org/10.1016/j.jocs.2022.101833
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Kurniabudi, Kurniabudi Deris, Stiawan Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Defit, Sarjon Triana, Yaya Sudarya Budiarto, Rahmat Improvement of attack detection performance on the internet of things with PSO-search and random forest
description	The presence of the internet of things allows various smart devices to be connected and interact with each other. Although IoT provides benefits in daily activities, however, with the presence of new technologies, IoT is vulnerable to new types of attacks. The massive IoT traffic results in a large number of traffic features and constructs complex network that makes intrusion detection systems (IDSs) require large resources to identify the type of attacks. On the other hand, most of the intrusion detection techniques are not feasible for IoT networks because they require more computing resources for attack detection, while IoT devices have limited computing resources and storage capacity. Thus, a lightweight IDS that has ability to identify new types of attacks is required. This research proposes a hybrid of Panigrahi and PSO-Search approaches to reduce the complexity of the network by eliminating the number of irrelevant features effectively and efficiently and combine with Random Forest optimization method to improve detection performance. The proposed IDS is validated with training and testing data, using hold-out, Stratified k-fold cross-validation, and percentage split test mode on CICIDS-2017 dataset MachineLearningCSV version. The dataset is chosen, as it represents real IoT network traffic data. Experimental results show that the performance improvement of the proposed hybrid IDS is very encouraging. The accuracy rate reaches 99.9 %, with an average Recall value of 1.000.
format	Article
author	Kurniabudi, Kurniabudi Deris, Stiawan Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Defit, Sarjon Triana, Yaya Sudarya Budiarto, Rahmat
author_facet	Kurniabudi, Kurniabudi Deris, Stiawan Darmawijoyo, Darmawijoyo Idris, Mohd. Yazid Defit, Sarjon Triana, Yaya Sudarya Budiarto, Rahmat
author_sort	Kurniabudi, Kurniabudi
title	Improvement of attack detection performance on the internet of things with PSO-search and random forest
title_short	Improvement of attack detection performance on the internet of things with PSO-search and random forest
title_full	Improvement of attack detection performance on the internet of things with PSO-search and random forest
title_fullStr	Improvement of attack detection performance on the internet of things with PSO-search and random forest
title_full_unstemmed	Improvement of attack detection performance on the internet of things with PSO-search and random forest
title_sort	improvement of attack detection performance on the internet of things with pso-search and random forest
publisher	Elsevier Ltd.
publishDate	2022
url	http://eprints.utm.my/103047/1/MohdYazidIdris2022_ImprovementofAttackDetectionPerformance.pdf http://eprints.utm.my/103047/ https://dx.doi.org/10.1016/j.jocs.2022.101833
_version_	1781777636987502592
score	13.211869

Improvement of attack detection performance on the internet of things with PSO-search and random forest

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