Open Access

ARTICLE

A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

1 Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64011, Iraq
2 College of Computer Sciences and Information Technology, University of Kerbala, Karbala, 56001, Iraq
3 Enginerring Technical College, Al-Ayen University, Thi-Qar, 64011, Iraq
4 Information Technology College, University of Babylon, Hilla, 51002, Iraq
5 Department of Computer Science, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq
6 Artificial Intelligence Engineering Department, College of Engineering, Al-Ayen University, Thi-Qar, 64001, Iraq
7 Department of Computer Science, Bayan University, Erbil, Kurdistan, 44001, Iraq

* Corresponding Author: Haydar Abdulameer Marhoon. Email:

(This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)

Computers, Materials & Continua 2025, 82(3), 4181-4218. https://doi.org/10.32604/cmc.2025.058822

Received 22 September 2024; Accepted 24 December 2024; Issue published 06 March 2025

Abstract

In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set, and this method uses more advanced machine learning models, like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks together, to see coIn our work, we used five different types of machine learning models. These are the forward artificial neural network (ANN), the CNN-LSTM hybrid models, the LR meta-model for linear regression, the Extreme Gradient Boosting (XGB) regression, and the ensemble model. We implemented Random Forest (RF), Gradient Boosting, and XGBoost as baseline models. To train and evaluate the five models, we used four possible features: the size of the circular area, the sensing range, the communication range, and the number of sensors for both Gaussian and uniform sensor distributions. We used Monte Carlo simulations to extract these traits. Based on the comparison, the CNN-LSTM model with Gaussian distribution performs best, with an R-squared value of 99% and Root mean square error (RMSE) of 6.36%, outperforming all the other models.

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Keywords

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