Research on the application of machine learning to intrusion detection in WSN

With the continuous development of computer and communication technologies, networks often face a variety of attacks. The distributed and wireless transmission characteristics of the Wireless Sensor Network (WSN) make it easy to suffer from network attacks, which brings a severe test for the design of the WSN security protection program. As an important means of network attack detection, intrusion detection is a proactive security protection technology and a key technology to ensure the security of WSN network environment. In recent years machine learning methods have made tremendous progress in many fields, and have achieved certain application research results in the field of WSN intrusion detection. In order to facilitate the in-depth study of WSN intrusion detection technology, this paper starts from the characteristics of WSN and the uniqueness of WSN intrusion detection research, and categorizes and synthesizes the relevant research in this field in recent years. First, the challenges and development status of the WSN are briefly introduced. Then, the challenges faced when intrusion detection is designed in WSNs are analyzed based on the characteristics of WSNs. Subsequently, literature review and categorization of research related to intrusion detection in WSNs are conducted, focusing on the categorization and discussion of applied research methods based on machine learning. Finally, the future prospects and directions of this research direction are discussed to provide valuable references for promoting in-depth research and practical applications in the field of WSN intrusion detection. © 2024 Science Press. All rights reserved.