Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (4): 18-27.DOI: 10.3778/j.issn.1002-8331.2010-0457

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

WANG Zhendong, ZHANG Lin, LI Dahai   

  1. Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • Online:2021-02-15 Published:2021-02-06



  1. 江西理工大学,江西 赣州 341000


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.

Key words: cyber security, Internet of things security, intrusion detection, machine learning, deep learning



关键词: 网络安全, 物联网安全, 入侵检测, 机器学习, 深度学习