Anomaly user detection method based on minimal risk deep neural network

Abstract

The invention discloses a method for detecting abnormal users based on a minimum-risk deep neural network. First, the data of abnormal users is preprocessed to obtain data with the same amount of data between abnormal users and normal users; secondly, the Mini-batch gradient descent method is used to detect The deep neural network is trained and combined with the degree of harm of different abnormal users in the NOMA communication system to construct a decision table based on minimum risk; finally, the loss function corresponding to different decisions set by the decision table is introduced into the deep neural network to construct a minimum risk-based Neural network anomaly user detection method. The present invention solves the problem of abnormal user detection in the NOMA system based on big data through the powerful representation and analysis capabilities of the deep neural network for high-dimensional data, introduces the minimum risk cost into the training of the deep neural network, and solves various abnormal users in the NOMA system It is expected to provide new ideas and theoretical innovations for abnormal user detection.

Description

technical field [0001] The invention belongs to the field of abnormal detection, and in particular relates to an abnormal user detection method based on a minimum risk deep neural network. Background technique [0002] Due to the openness of wireless channels, with the development of wireless communication technology, there are more and more security problems. If there is no effective response strategy, it may cause immeasurable losses to wireless communication networks and legitimate users. Non-orthogonal multiple access (NOMA) technology has become one of the key technologies of the next generation mobile communication system (5G). With the development of NOMA, security issues in NOMA have also begun to receive attention and research. In the power domain NOMA, a corresponding power allocation scheme is derived based on the user's channel state information (Channel State Information, CSI), and superimposed information transmission is performed on the same frequency spectr...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to classify and detect abnormal users, propose a method for detecting abnormal users based on a deep neural network with minimum risk, realize the detection of abnormal users, and solve the problem that traditional abnormal user detection methods are difficult to achieve high monitoring efficiency and lack of Technical issues of different loss decisions for various abnormal users

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