The Journal of Science and Engineering Applications (JSEA)

Abstract

Intrusion Detection Systems (IDS) are critical for maintaining the security and integrity of Wireless Sensor Networks (WSNs) which are susceptible to various cyber threats. This study evaluates the performance of three machine learning models Random Forest, Decision Tree, and Logistic Regression in classifying network traffic within a WSN environment. A comprehensive analysis was conducted using a confusion matrix to derive key performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest model demonstrated superior performance across all metrics by achieving an accuracy of 99.63% and a ROC-AUC of 0.9970 indicating its robustness in distinguishing between normal and malicious traffic. These findings underscore the efficacy of ensemble learning methods particularly Random Forest in enhancing IDS capabilities within WSNs.