A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks.
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| Title: | A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks. |
|---|---|
| Authors: | Loughmari, Mohamed1 mohamed.loughmari@usmba.ac.ma, El Affar, Anass1 anass.elaffar@usmba.ac.ma |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Apr2025, Vol. 15 Issue 2, p2089-2097. 9p. |
| Subjects: | Wireless sensor network security, Machine learning, Wireless sensor networks, Feature selection, Denial of service attacks |
| Abstract: | Wireless sensor networks (WSNs) are increasingly prevalent in the Internet of Things ecosystem and have been used in several fields such as environmental monitoring, military, and healthcare. However, their limited resources and distributed architecture remain two main challenges: energy and security. Furthermore, denial of service (DoS) attacks are one of the principal cyber threats to WSNs. This research proposes a lightweight machine learning (ML) approach based on the extreme gradient boosting (XGBoost) model to detect these attacks in WSNs. Through an extensive investigation, we evaluate four prominent ML algorithms: random forest (RF), k-nearest neighbor (KNN), stochastic gradient descent (SGD), and XGBoost, using the WSN-DS dataset. In addition, we implement and investigate several feature selection techniques in order to have an improved version of the original dataset. Moreover, we evaluate the performance using various performance metrics, which include accuracy, precision, recall, F1-score, and processing time. The latter is a crucial consideration in WSN environments. For validation, we have employed 5-fold cross-validation to ensure robust and reliable results. The proposed model has achieved good performance in all metrics, with a maximum accuracy of up to 99.73%, and a 68% lower processing time compared to the other investigated classifiers. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183183761 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Loughmari%2C+Mohamed%22">Loughmari, Mohamed</searchLink><relatesTo>1</relatesTo><i> mohamed.loughmari@usmba.ac.ma</i><br /><searchLink fieldCode="AR" term="%22El+Affar%2C+Anass%22">El Affar, Anass</searchLink><relatesTo>1</relatesTo><i> anass.elaffar@usmba.ac.ma</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Apr2025, Vol. 15 Issue 2, p2089-2097. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wireless+sensor+network+security%22">Wireless sensor network security</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Denial+of+service+attacks%22">Denial of service attacks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Wireless sensor networks (WSNs) are increasingly prevalent in the Internet of Things ecosystem and have been used in several fields such as environmental monitoring, military, and healthcare. However, their limited resources and distributed architecture remain two main challenges: energy and security. Furthermore, denial of service (DoS) attacks are one of the principal cyber threats to WSNs. This research proposes a lightweight machine learning (ML) approach based on the extreme gradient boosting (XGBoost) model to detect these attacks in WSNs. Through an extensive investigation, we evaluate four prominent ML algorithms: random forest (RF), k-nearest neighbor (KNN), stochastic gradient descent (SGD), and XGBoost, using the WSN-DS dataset. In addition, we implement and investigate several feature selection techniques in order to have an improved version of the original dataset. Moreover, we evaluate the performance using various performance metrics, which include accuracy, precision, recall, F1-score, and processing time. The latter is a crucial consideration in WSN environments. For validation, we have employed 5-fold cross-validation to ensure robust and reliable results. The proposed model has achieved good performance in all metrics, with a maximum accuracy of up to 99.73%, and a 68% lower processing time compared to the other investigated classifiers. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v15i2.pp2089-2097 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 2089 Subjects: – SubjectFull: Wireless sensor network security Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Wireless sensor networks Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Denial of service attacks Type: general Titles: – TitleFull: A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Loughmari, Mohamed – PersonEntity: Name: NameFull: El Affar, Anass IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 15 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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