TY  - EJOU
AU  - Marhoon, Haydar Abdulameer 
AU  - Sagban, Rafid 
AU  - Oudah, Atheer Y. 
AU  - Ahmed, Saadaldeen Rashid 

TI  - A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Intrusion detection system (IDS); hybrid models of CNN-LSTM; WSN; extreme gradient boosting (XGBoost) regressor; ensemble model

DO  - 10.32604/cmc.2025.058822