Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats an...
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Autor*in:	Rajiv Yadav [verfasserIn] Indu Sreedevi [verfasserIn] Daya Gupta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	WSNs Feature selection Classification techniques NSL-KDD dataset Accuracy IoT

Übergeordnetes Werk:	In: Alexandria Engineering Journal - Elsevier, 2016, 65(2023), Seite 461-473
Übergeordnetes Werk:	volume:65 ; year:2023 ; pages:461-473

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.aej.2022.10.033

Katalog-ID:	DOAJ080424627

Internformat


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520			\|a Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.
650		4	\|a WSNs
650		4	\|a Feature selection
650		4	\|a Classification techniques
650		4	\|a NSL-KDD dataset
650		4	\|a Accuracy
650		4	\|a IoT
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Indu Sreedevi \|e verfasserin \|4 aut
700	0		\|a Daya Gupta \|e verfasserin \|4 aut
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allfieldsSound	10.1016/j.aej.2022.10.033 doi (DE-627)DOAJ080424627 (DE-599)DOAJ112fffc6e02a4c818d268e851b80114c DE-627 ger DE-627 rakwb eng TA1-2040 Rajiv Yadav verfasserin aut Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame. WSNs Feature selection Classification techniques NSL-KDD dataset Accuracy IoT Engineering (General). Civil engineering (General) Indu Sreedevi verfasserin aut Daya Gupta verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 65(2023), Seite 461-473 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:65 year:2023 pages:461-473 https://doi.org/10.1016/j.aej.2022.10.033 kostenfrei https://doaj.org/article/112fffc6e02a4c818d268e851b80114c kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016822006871 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 65 2023 461-473
language	English
source	In Alexandria Engineering Journal 65(2023), Seite 461-473 volume:65 year:2023 pages:461-473
sourceStr	In Alexandria Engineering Journal 65(2023), Seite 461-473 volume:65 year:2023 pages:461-473
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	WSNs Feature selection Classification techniques NSL-KDD dataset Accuracy IoT Engineering (General). Civil engineering (General)
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container_title	Alexandria Engineering Journal
authorswithroles_txt_mv	Rajiv Yadav @@aut@@ Indu Sreedevi @@aut@@ Daya Gupta @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	669887609
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callnumber-first	T - Technology
author	Rajiv Yadav
spellingShingle	Rajiv Yadav misc TA1-2040 misc WSNs misc Feature selection misc Classification techniques misc NSL-KDD dataset misc Accuracy misc IoT misc Engineering (General). Civil engineering (General) Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques
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title	Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques
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title_sort	augmentation in performance and security of wsns for iot applications using feature selection and classification techniques
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title_auth	Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques
abstract	Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.
abstractGer	Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.
abstract_unstemmed	Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.
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title_short	Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques
url	https://doi.org/10.1016/j.aej.2022.10.033 https://doaj.org/article/112fffc6e02a4c818d268e851b80114c http://www.sciencedirect.com/science/article/pii/S1110016822006871 https://doaj.org/toc/1110-0168
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Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">WSNs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">NSL-KDD dataset</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Accuracy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IoT</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). 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Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

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