A method for identifying malicious nodes in edge computing
A malicious node and identification method technology, applied in digital transmission systems, data exchange networks, electrical components, etc., can solve problems that affect identification accuracy, low identification accuracy, and time-consuming
Active Publication Date: 2021-12-21
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1
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However, in practical applications, the limited acquisition of channel information data makes it time-consuming when there is a certain amount of data required in feature extraction. If the amount of data is insufficient, the recognition accuracy will be low, which will affect the recognition accuracy.
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[0023] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the channel frequency response based on deep neural network to identify malicious nodes, but the scope of protection of the present invention is not limited to the following .
[0024] Such as figure 1 As shown, deep neural networks have excellent fitting and classification capabilities, so using deep neural networks for malicious node identification has good performance. However, when the data set is relatively small, the deep neural network has its limitations, and the time correlation requirements for wireless channel information, or some other restrictive requirements, cannot obtain a relatively large channel sample set. Well, in cases where it is important to obtain a sufficient dataset from collecting channel responses within the relevant tim...
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The invention discloses a method for identifying malicious nodes in edge computing, which includes collecting channel information data sets of the Kth node, generating an input sample set after average data enhancement, generating an output sample set after average sample construction, and constructing new channel information Data set for recognition training and other steps. The present invention uses the correlation between the collected continuous multi-frame channel information to construct a new channel response information vector, that is, to average two or more time slot channel frequency response vectors to obtain a new channel response vector, It overcomes the disadvantage of low recognition rate caused by insufficient data volume in extracting channel features from channel information to identify malicious nodes.
Description
technical field [0001] The invention relates to edge computing security computing, in particular to a method for identifying malicious nodes of edge computing. Background technique [0002] Edge computing, with its near-node deployment and the characteristics of the Internet of Things being close to the nodes of the Internet of Things, overcomes the long-distance transmission delay and computing load of the cloud computing center, slows down network congestion, and migrates some or all of the computing tasks of the original cloud computing model To network edge devices, the Internet of Things can better meet the needs of massive edge data, real-time, privacy protection, energy consumption and other major aspects. Therefore, edge computing has rich application scenarios, such as smart home, video surveillance, smart medical care , intelligent transportation, unmanned factories, smart grid and other applications. [0003] However, edge computing is close to many nodes. Nodes ...
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IPC IPC(8): H04L29/06H04L12/24
CPCH04L63/1416H04L63/1408H04L41/14
Inventor 许爱东蒋屹新文红张宇南伊玉君
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD