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Deep learning in security of internet of things - 2021

Research Area:  Machine Learning

Abstract:

Internet of Things (IoT) technology is increasingly prominent in the current stage of social development. All walks of life have begun to implement the Internet of Things integration technology, so as to strive to promote industrial modernization, intelligence and digitalization. In this case, how to link high-risk network activities with entities has become the primary issue for promoting industrial development. However, at this stage, the security issues in the development of the IoT technology have contradictions that are difficult to resolve. According to this situation, how to make system defense intelligent and replace manual monitoring has become the future of the development of security architecture. This paper combines existing security research to explore the possibility of deep learning (DL) in upgrading the IoT security architecture, discusses how the Internet of Things can identify and respond to cyber attacks, and how to encrypt edge data transmission. Moreover, this paper discusses security research in application fields such as Industrial Internet of Things, Internet of Vehicles, smart grid, smart home and smart medical. Then we summarized the areas that can be improved in future technological development, including sharing computing power through the edge NPU central device and closely combining the environmental simulation model with the actual environment, as well as malicious code detection, intrusion detection, production safety, vulnerability detection, fault diagnosis and blockchain technology.

Author(s) Name:  Yuxi Li; Yue Zuo; Houbing Song; Zhihan Lv

Journal name:   IEEE Internet of Things Journal

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/JIOT.2021.3106898

Volume Information:  Page(s): 1 - 1