ICCK Transactions on Wireless Networks
ISSN: 3068-7721 (Online)
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TY - JOUR AU - Sharma, Anshika AU - Rani, Shalli PY - 2025 DA - 2025/06/19 TI - An Optimized Ensemble Approach for Securing Wireless Sensor Networks Against Attacks JO - ICCK Transactions on Wireless Networks T2 - ICCK Transactions on Wireless Networks JF - ICCK Transactions on Wireless Networks VL - 1 IS - 1 SP - 5 EP - 15 DO - 10.62762/TWN.2025.109626 UR - https://www.icck.org/article/abs/TWN.2025.109626 KW - wireless sensor networks KW - intrusion detection system KW - security KW - machine learning KW - deep learning KW - WSN-DS dataset AB - Wireless Sensor Networks (WSNs) are prone to different security threats because of their open communication environment, distributed architecture, and resource constraints. For the security and integrity of a network to be ensured, robust intrusion detection systems (IDS) are required. The WSN-DS dataset has been used to provide an effective machine learning (ML) and Deep Learning (DL) based IDS and attack detection technique for WSNs. Several learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Logistic Regression (LR), and Neural Networks (NN), are compared in terms of performance. Preprocessing methods of data encoding, normalization, and data splitting are applied to the dataset to improve classification performance. The effectiveness of these models is compared using large trials with significant performance metrics such as ROC-AUC, F1-score, accuracy, precision, and recall. The results indicate that the Optimized RF model has been optimized to achieve the optimal accuracy of 99.71\%, which outperforms other state-of-the-art approaches. Apart from pointing out the importance of ML in detecting WSN attacks, this research provides a promising way forward for enhancing network security through effective detection methods. SN - 3068-7721 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Sharma2025An,
author = {Anshika Sharma and Shalli Rani},
title = {An Optimized Ensemble Approach for Securing Wireless Sensor Networks Against Attacks},
journal = {ICCK Transactions on Wireless Networks},
year = {2025},
volume = {1},
number = {1},
pages = {5-15},
doi = {10.62762/TWN.2025.109626},
url = {https://www.icck.org/article/abs/TWN.2025.109626},
abstract = {Wireless Sensor Networks (WSNs) are prone to different security threats because of their open communication environment, distributed architecture, and resource constraints. For the security and integrity of a network to be ensured, robust intrusion detection systems (IDS) are required. The WSN-DS dataset has been used to provide an effective machine learning (ML) and Deep Learning (DL) based IDS and attack detection technique for WSNs. Several learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Logistic Regression (LR), and Neural Networks (NN), are compared in terms of performance. Preprocessing methods of data encoding, normalization, and data splitting are applied to the dataset to improve classification performance. The effectiveness of these models is compared using large trials with significant performance metrics such as ROC-AUC, F1-score, accuracy, precision, and recall. The results indicate that the Optimized RF model has been optimized to achieve the optimal accuracy of 99.71\\%, which outperforms other state-of-the-art approaches. Apart from pointing out the importance of ML in detecting WSN attacks, this research provides a promising way forward for enhancing network security through effective detection methods.},
keywords = {wireless sensor networks, intrusion detection system, security, machine learning, deep learning, WSN-DS dataset},
issn = {3068-7721},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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