A Study on Machine Learning for Intrusion Detection in Wireless Sensor Networks: Enhancing Network Security and Threat Detection

Abstract

Wireless Sensor Networks (WSNs) are widely used in healthcare, environmental monitoring, smart cities, agriculture, military surveillance, and industrial automation. However, their wireless communication, limited computing resources, distributed architecture, and unattended deployment make them vulnerable to attacks such as denial of service, spoofing, selective forwarding, sinkhole, wormhole, Sybil, and routing attacks. Traditional intrusion detection systems often depend on predefined signatures and may fail to identify new or evolving threats. This study proposes a machine learning-based intrusion detection system for improving the security of WSNs. The proposed approach collects relevant network traffic features, including packet transmission rate, packet loss, energy consumption, routing behaviour, node activity, and communication patterns. Data preprocessing techniques are applied to remove noise, handle missing values, normalize features, and reduce dimensionality. Supervised machine learning algorithms, such as Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Network, can then be trained to distinguish normal network behaviour from malicious activities. Their performance is evaluated using accuracy, precision, recall, F1-score, false-positive rate, detection time, and computational overhead. The framework also considers the resource limitations of sensor nodes by prioritizing lightweight feature selection and efficient classification methods. Experimental evaluation using WSN traffic datasets or simulated attack scenarios is expected to demonstrate that machine learning can improve attack detection while reducing false alarms compared with conventional security mechanisms. The proposed system can support early threat identification, strengthen network reliability, and extend network lifetime by preventing malicious resource consumption. This research provides a scalable and adaptive foundation for developing intelligent intrusion detection solutions for secure, resource-constrained, and dynamically changing wireless sensor network environments.