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Efficient autonomous defense System Using Machine Learning on Edge Device

Computers, Materials & Continua 2022年第2期70卷 3565-3588页

作者： Jaehyuk Cho School of Electronic Engineering Soongsil UniversitySeoul06978Korea

As a large amount of data needs to be processed and speed needs to be improved,edge computing with ultra-low latency and ultra-connectivity is emerging as a new *** changes can lead to new cyber risks,and should therefore be considered for a security threat *** this end,we constructed an edge system to study security in two directions,hardware and ***,on the hardware side,we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array(FPGA)which is suitable for edge computing and identifying communication status to control the communication method according to *** addition,on the software side,data collected on the server performs end-to-end encryption via symmetric encryption ***,we modeled autonomous defense systems on the server by using machine learning which targets to incoming and outgoing *** log utilizes existing intrusion detection datasets that should be used in real-world *** log was used to detect intrusion early by modeling an intrusion prevention system to identify behaviors that violate security policy,and to utilize the existing intrusion detection data set that should be used in a real *** this,we designed an efficient autonomous defense system that can provide a stable system by detecting abnormal signals from the device and converting them to an effective method to control edge computing,and to detect and control abnormal intrusions on the server side.

关键词： autonomous defense side channel attack intrusion prevention system edge computing machine learning

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