A Comprehensive Analysis of Security Challenges and Countermeasures in Wireless Sensor Networks Enhanced by Machine Learning and Deep Learning Technologies

Abstract

Wireless sensor networks (WSNs) play a crucial role in monitoring and capturing information in various domains, including the Internet of Medical Things (IOMT). However, WSNs face significant security challenges, such as intrusion and potential malicious activities, due to their distributed and resource-constrained nature. This research provides a comprehensive analysis of the security challenges in WSNs enhanced by machine learning and deep learning technologies within the context of the IOMT. The research addresses these challenges by proposing practical countermeasures to mitigate security concerns in WSNs. It discusses the complexities, critical security issues, and vulnerabilities within WSNs, offering insights into potential fixes based on various approaches and theories. Furthermore, the integration of machine learning and deep learning in WSNs enables efficient communication, data analysis, and control, supporting areas like the IOMT, which includes monitoring prescription orders, tracking patients' movements, and remotely managing patients with chronic illnesses. By addressing the security challenges specific to WSNs in the IOMT environment, this research contributes to the advancement of secure and reliable wireless sensing systems in critical domains. The utilization of machine learning and deep learning technologies facilitates the development of robust methods for detecting and mitigating network attacks. The practical implications of the research findings are demonstrated through tangible examples within the IOMT context, emphasizing the potential impact on improving the security and reliability of wireless sensing systems.