Unveiling the Invisible: Powering Security Threat Detection in WSN With AI

Abstract

Security in wireless sensor networks (WSNs) is of paramount importance due to their pervasive deployment in critical infrastructure and sensitive environments. Despite their ubiquitous nature, WSNs are vulnerable to various security threats, ranging from unauthorized access to data manipulation and network disruption. In response to these challenges, this paper proposes a novel approach leveraging the Base Stacked Long Short-Term Memory with Attention Models and AdaBoost Ensemble (BSLAM-AE) architecture to enhance security in WSNs. The proposed model is designed to address the unique characteristics and challenges of WSNs, combining deep learning and ensemble learning techniques to detect and mitigate security threats effectively. The BSLAM-AE model incorporates stacked LSTM networks with attention mechanisms, enabling the analysis of time-series data and the detection of subtle anomalies or security breaches. In addition, an AdaBoost ensemble-learning component iteratively trains a set of models to improve predictive accuracy and robustness. Implemented in the PyCharm integrated development environment, experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy of 98% in detecting security threats in WSNs. Overall, the BSLAM-AE model represents a significant advancement in WSN security, offering a comprehensive and efficient solution for detecting and mitigating security threats. By leveraging deep learning and ensemble learning techniques, the proposed model provides enhanced security and reliability, thereby safeguarding WSNs against potential attacks and ensuring the integrity and availability of critical data and infrastructure.