Detecting network security incidents in wireless sensor networks using machine learning

Abstract

This study enhances the domain of cybersecurity within wireless sensor networks (WSNs) through the integration of sophisticated artificial intelligence (AI) and machine learning (ML) techniques. By conducting an exploratory data analysis (EDA), this research reveals critical insights into network behavior, facilitating the development of predictive models for anomaly detection. The application of ML algorithms decision trees (DT) and random forest (RF) demonstrated dominant performance in identifying potential security threats, as evidenced by metrics accuracy, precision, recall, and F1 scores. This work not only enhances the security framework for WSNs but also contributes to the extensive field of network security, offering a robust analytical and predictive methodology for future cybersecurity initiatives. The advanced model can be deployed in other WSN and internet of things (IoT) based applications.