Machine Learning Techniques for Robust Security in Wireless Sensor Networks

Abstract: The security of wireless sensor networks (WSNs) is critical for ensuring the reliability and accuracy of real-time monitoring systems. A key vulnerability in WSNs is the risk of system breakdown or erroneous decision-making, which can lead to adverse consequences. Given the sensitivity of data handled by these networks, robust protection against various attacks and intrusions is imperative. However, existing security algorithms often fall short when applied to large-scale WSNs due to challenges such as high energy consumption, limited throughput, and excessive computational overhead. To address these challenges, this research introduces an intelligent middleware layer designed to enhance security in WSNs. The proposed solution utilizes Generative Adversarial Networks (GANs), an unsupervised machine learning technique, featuring generator (G) and discriminator (D) networks working adversarially to enhance WSN security. This intelligent middleware acts as a protective layer between WSNs and end-users, addressing vulnerabilities and mitigating threats. The approach ensures robust security while maintaining energy efficiency and scalability for real-world applications. By integrating advanced machine learning techniques, this research establishes a foundation for creating resilient and trustworthy wireless sensor networks capable of handling modern challenges effectively.