A Survey of Machine Learning-based loT Intrusion Detection Techniques

被引:1
作者
Long, Jing [1 ,2 ,3 ]
Fang, Fei [1 ]
Luo, Haibo [3 ]
机构
[1] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China
[2] Guilin Univ Elect Technol, Guangxi Key Lab Cryptog & Informat Secur, Guilin 541004, Peoples R China
[3] Minjiang Univ, Fujian Prov Key Lab Informat Proc & Intelligent C, Fuzhou 350121, Peoples R China
来源
2021 IEEE 6TH INTERNATIONAL CONFERENCE ON SMART CLOUD (SMARTCLOUD 2021) | 2021年
基金
中国国家自然科学基金;
关键词
Intrusion Detection; Machine Learning; Security; IOT; DETECTION SYSTEM; ALGORITHM;
D O I
10.1109/SmartCloud52277.2021.00009
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Research on data anomaly intrusion detection in the Internet of Things (loT) environment is still insufficient, and there are still many practical problems that need to be solved urgently. At present, the deep integration of (AI) technology and loT, and intelligent technologies such as machine learning have gradually been applied to the field of intrusion detection of the Internet of Things. Therefore, this paper conducts the latest and in-depth research on the efficient and accurate intrusion detection technology used in computers, analyzes the security threats facing the Internet of Things, and summarizes the latest technology and evaluation indicators of intrusion detection. This paper can promote the improvement of large-scale industrial network security detection performance under the new situation to a certain extent and has important theoretical significance and practical application value for ensuring the rapid and healthy development of today's industrial loT security environment.
引用
收藏
页码:7 / 12
页数:6
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