Comparative analysis of CatBoost, LightGBM, XGBoost, RF, and DT methods optimised with PSO to estimate the number of k-barriers for intrusion detection in wireless sensor networks

Abstract The protection of borders is a critical concern for all countries, and Wireless Sensor Networks (WSNs) play a crucial role in assuring security by enabling intrusion detection and surveillance at border regions. This study presents an effective machine learning model designed to predict the number of k-barriers for rapid and robust intrusion detection and prevention in a rectangular area utilizing features extracted from a WSN through Monte-Carlo simulation. The proposed model is implemented using the CatBoost method, which is optimized with the metaheuristic Particle Swarm Optimisation (PSO) algorithm. To ensure a fair assessment, the performance of the proposed model is compared with…

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  Türkiye Bilimsel ve Teknolojik Araştırma Kurumu