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Tackling the anomaly detection challenge in large-scale wireless sensor networks.

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Title: Tackling the anomaly detection challenge in large-scale wireless sensor networks.
Authors: Zhukabayeva, Tamara1,2,3 tamara.kokenovna@gmail.com, Adamova, Aigul1,3 aigul.adamova@astanait.edu.kz, Zholshiyeva, Lyazzat1 lazzat.zhol.81@gmai.com, Mardenov, Yerik1,4 emardenov@gmail.com, Karabayev, Nurdaulet1,2 222240@astanait.edu.kz, Baumuratova, Dilaram1,4 baumuratova.d@gmail.com
Source: International Journal of Electrical & Computer Engineering (2088-8708). Apr2025, Vol. 15 Issue 2, p2479-2490. 12p.
Subjects: Wireless sensor network security, Anomaly detection (Computer security), Detection algorithms, K-nearest neighbor classification, Machine learning
Abstract: One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbors (KNN) and Z score methods are evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z score methodology showed a 98.9% level of accuracy, which was much superior to the KNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN. [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
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DbLabel: Engineering Source
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  Data: Tackling the anomaly detection challenge in large-scale wireless sensor networks.
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  Data: <searchLink fieldCode="AR" term="%22Zhukabayeva%2C+Tamara%22">Zhukabayeva, Tamara</searchLink><relatesTo>1,2,3</relatesTo><i> tamara.kokenovna@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Adamova%2C+Aigul%22">Adamova, Aigul</searchLink><relatesTo>1,3</relatesTo><i> aigul.adamova@astanait.edu.kz</i><br /><searchLink fieldCode="AR" term="%22Zholshiyeva%2C+Lyazzat%22">Zholshiyeva, Lyazzat</searchLink><relatesTo>1</relatesTo><i> lazzat.zhol.81@gmai.com</i><br /><searchLink fieldCode="AR" term="%22Mardenov%2C+Yerik%22">Mardenov, Yerik</searchLink><relatesTo>1,4</relatesTo><i> emardenov@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Karabayev%2C+Nurdaulet%22">Karabayev, Nurdaulet</searchLink><relatesTo>1,2</relatesTo><i> 222240@astanait.edu.kz</i><br /><searchLink fieldCode="AR" term="%22Baumuratova%2C+Dilaram%22">Baumuratova, Dilaram</searchLink><relatesTo>1,4</relatesTo><i> baumuratova.d@gmail.com</i>
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  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, p2479-2490. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Wireless+sensor+network+security%22">Wireless sensor network security</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbors (KNN) and Z score methods are evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z score methodology showed a 98.9% level of accuracy, which was much superior to the KNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN. [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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        Value: 10.11591/ijece.v15i2.pp2479-2490
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        Text: English
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        PageCount: 12
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      – SubjectFull: Anomaly detection (Computer security)
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      – SubjectFull: Detection algorithms
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      – SubjectFull: K-nearest neighbor classification
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      – SubjectFull: Machine learning
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              Text: Apr2025
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