Smart Security: AI Transformations in Wireless Sensor Networks

- Wireless Sensor Networks (WSNs) have seen widespread adoption as a key technology for collecting and analyzing environmental data. Their flexibility and diverse applications — including critical military operations, forest fire detection, healthcare monitoring, and civilian usage — have made them a major area of research. The ability of WSNs to deliver valuable insights and tackle real-world challenges has attracted strong interest within the scientific community. However, WSNs are vulnerable to various security threats, especially in unattended or remote environments. To mitigate these threats, this paper integrates the widely used Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with machine learning techniques. The study analyzes several machine learning algorithms, considering multiple parameter classes, and evaluates their suitability for WSN environments. Among the techniques analyzed, the Random Forest algorithm proves to be the most effective for intrusion detection, capable of identifying attacks such as blackhole, grayhole, TDMA, and flooding attacks. This method uses six key extracted features and achieves an overall accuracy of 99.12%, with individual attack prediction accuracies of 87%, 89%, 92%, and 93%, respectively. Rather than merely detecting anomalies, the integration of this system with a centralized management model transforms it into a smart governance solution. This approach not only prevents various types of intrusions but also enhances crucial network parameters such as energy efficiency, network lifetime, throughput, overall performance, and security. Keywords— LEACH, WSN, Logistic Regression, KNN, SVM, Random Forest, Decision Tree, XGBoost, DoS.