Enhancing Wireless Sensor Network Security with Machine Learning

Energy consumption and security pose significant challenges in wireless sensor networks (WSNs), often operating in opposition. As security measures become more intricate, they tend to amplify battery drainage. Traditional security protocols, reliant on encryption and key management, prove ineffective in WSNs due to the dynamic nature of sensor communication and network topology shifts. Consequently, machine learning (ML) algorithms emerge as potential solutions to bolster security by incorporating monitoring and decision intelligence. However, ML algorithms introduce additional hurdles, including training complexities and data requirements. This study serves as a comprehensive resource on WSN infrastructure and its security challenges, exploring the potential of ML algorithms to mitigate security costs across various domains. It also delves into challenges and proposed solutions for enhancing sensor capabilities to detect threats, attacks, risks, and malicious nodes through continuous learning and self-development using ML algorithms. Additionally, the study addresses open issues regarding the adaptation of ML algorithms to suit sensor capabilities in WSNs.