机器学习在WSN入侵检测中的应用研究

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

随着计算机、通信技术的不断发展,网络经常面临各种各样的攻击。无线传感器网络(Wireless Sensor Network,WSN)的分布式和无线传输等特性使其易于遭受网络攻击,为WSN安全防护方案设计带来了严峻考验。入侵检测是一种积极主动的安全防护技术,是网络攻击检测的重要手段,是保障WSN网络环境安全的关键技术。近年来,机器学习方法在许多领域都取得了巨大的发展,在WSN入侵检测领域取得了一定的应用研究成果。为了便于对WSN入侵检测技术进行深入研究,从WSN的特点和WSN入侵检测研究的独特性出发,对近些年该领域的相关研究进行分类综述。首先,简要介绍了WSN所面临的挑战和发展现状。然后,根据WSN的特点分析了入侵检测在WSN中设计时面临的挑战。随后对WSN入侵检测相关研究进行文献综述与分类,重点对基于机器学习的应用研究方法进行分类论述与探讨。最后,讨论该研究方向未来发展前景与方向,为推动WSN入侵检测领域深入研究与实际应用提供参考。

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.