Development of an Ensemble Tree-Based Intrusion Detection Model for Enhanced Security in Wireless Sensor Networks

More and more mission-critical applications are relying on wireless sensor networks, or WSNs for short. Among these uses are environmental monitoring, industrial automation, and military surveillance. Conversely, WSNs are susceptible to a variety of security risks because to their open and dispersed design. Using an ensemble tree-learning technique, this research presents a novel intrusion detection model. Improving the robustness and safety of WSNs is the driving force behind this study. The proposed model maximises detection accuracy while minimizing false positives by combining the strengths of the Random Forest, Gradient Boosted Trees, and Extra Trees classifiers. To improve the model's performance, the Hybrid Feature Selection Algorithm (HyFSA) is used for feature selection and dimensionality reduction. Based on the experimental findings, the suggested ensemble model outperforms the traditional single-classifier approaches in terms of F1-score, recall, accuracy, and precision.