A Hybrid Artificial Intelligence Approach for Intrusion Detection in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) play a vital role in modern applications such as environmental monitoring, industrial automation, and smart infrastructure, where reliable data transmission, robustness, and energy efficiency are essential. However, their distributed architecture and constrained computational resources make them highly vulnerable to a wide range of security threats, including Blackhole, Grayhole, Flooding, and Scheduling attacks. These attacks can severely disrupt network functionality, degrade data integrity, and compromise the overall reliability of mission-critical operations. To address these challenges, we present an intrusion detection system (IDS) framework that leverages a diverse set of machine learning (ML) models, incorporating both boosting and non-boosting techniques, as well as deep learning (DL) architectures, including sequential and non-sequential designs. This diversity enables the framework to capture varied learning behaviors and decision boundaries. To further enhance detection accuracy and adaptability, Ant colony optimization (ACO) is employed as a metaheuristic tuning layer, refining hyperparameters to improve performance under the strict resource limitations typical of WSN environments. Each model is evaluated in both its baseline and ACO-optimized form, enabling a detailed comparative analysis that highlights the influence of optimization on intrusion detection effectiveness. Experimental results demonstrate that ACO significantly strengthens model resilience against diverse threats, offering an adaptive and efficient approach to securing modern WSN infrastructures.