Advancing IoT Security: Integrative Machine Learning Models for Enhanced Intrusion Detection in Wireless Sensor Networks

This paper introduces a breakthrough approach to enhancing intrusion detection capabilities within Wireless Sensor Networks (WSNs) by implementing the Enhanced Wireless Intrusion Detection System (EW-IDS). Leveraging a sophisticated blend of Machine Learning (ML) algorithms, including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), the proposed model effectively streamlines feature selection, resulting in a robust detection framework. Extensive evaluations demonstrate that EW-IDS not only achieves a high accuracy rate of 96%, but also consistently surpasses traditional models in precision, recall, and F1 Score metrics. These achievements underscore the model’s superior ability to differentiate between benign and malicious network activities. The implementation of EW-IDS marks a significant advance in securing the Internet of Things (IoT) environments against a diverse range of cyber threats, enhancing both the security protocols and operational efficiency of WSNs. This study provides a novel intrusion detection solution and offers valuable insights into the application of ML in complex security settings.