RETRACTED: Mobile network intrusion detection for IoT system based on transfer learning algorithm (Retracted Article)

被引:81
|
作者
Deng, Lianbing [1 ,2 ]
Li, Daming [3 ,4 ,5 ]
Yao, Xiang [2 ]
Cox, David [6 ]
Wang, Haoxiang [7 ,8 ]
机构
[1] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China
[2] Zhuhai Da Hengqin Sci & Technol Dev Co Ltd, Hengqin, Peoples R China
[3] Zhuhai Da Hengqin Sci & Technol Dev Co Ltd, Postdoctoral Res Ctr, Hengqin, Peoples R China
[4] City Univ Macau, Taipa, Macau, Peoples R China
[5] Int Postdoctoral Sci & Technol Res Inst Co Ltd, Wuhan, Hubei, Peoples R China
[6] Harvard Univ, Harvard John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[7] Cornell Univ, Ithaca, NY USA
[8] GoPercept Lab, New York, NY USA
关键词
Intrusion detection; Internet of things; Information security; Pop learning;
D O I
10.1007/s10586-018-1847-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The open deployment environment and limited resources of the Internet of things (IoT) make it vulnerable to malicious attacks, while the traditional intrusion detection system is difficult to meet the heterogeneous and distributed features of the Internet of things. The security and privacy protection of IoT is directly related to the practical application of IoT. In this paper, We analyze the characteristics of networking security and security problems, and discuss the system framework of Internet security and some key security technologies, including key management, authentication and access control, routing security, privacy protection, intrusion detection and fault tolerance and intrusion etc. This paper introduces the current problems of IoT in network security, and points out the necessity of intrusion detection. Several kinds of intrusion detection technologies are discussed, and its application on IoT architecture is analyzed. We compare the application of different intrusion detection technologies, and make a prospect of the next phase of research. Using data mining and machine learning methods to study network intrusion technology has become a hot issue. A single class feature or a detection model is very difficult to improve the detection rate of network intrusion detection. The performance of the proposed model is validated through the public databases.
引用
收藏
页码:S9889 / S9904
页数:16
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