基于机器学习的物联网入侵检测系统综述

Survey of Intrusion Detection Systems for Internet of Things Based on Machine Learning

物联网技术的广泛应用在给人们带来便利的同时也造成诸多安全问题,亟需建立完整且稳定的系统来确保物联网的安全,使得物联网对象间能够安全有效地通信,而入侵检测系统成为保护物联网安全的关键技术。随着机器学习和深度学习技术的不断发展,研究人员设计了大量且有效的入侵检测系统,对此类研究进行了综述。比较了现阶段物联网安全与传统的系统安全之间的不同;从检测技术、数据源、体系结构和工作方式等方面对入侵检测系统进行了详细分类;从数据集入手,对现阶段基于机器学习的物联网入侵检测系统进行了阐述;探讨了物联网安全的未来发展方向。

While the use of the Internet of things technology brings people convenience in life,it also brings many security problems.Therefore,a complete and robust system should be established to protect the security of the Internet of things,so that the objects of the Internet of things can communicate safely and effectively.The detection system has become a key technology to protect the security of the Internet of things.With the continuous development of machine learning and deep learning,researchers have designed a large number of effective intrusion detection systems.This paper reviews these studies.Firstly,the differences between the current Internet of things security and traditional system security are compared.Secondly,the intrusion detection system is classified in detail from the detection technology,data source,architecture and working methods.Thirdly,starting from the data set,the current stage of the Internet of things intrusion detection system based on machine learning is explained.Finally,the future development direction is discussed.