Malicious user identification method based on som neural network in cognitive radio

Abstract

The invention discloses a malicious user discrimination method based on a self-organizing map neural network in a cognitive radio. The invention uses a self-organizing map (referred to as SOM) neural network to learn the distribution characteristics of an input energy matrix, and calculates the input amount according to the learning result. effective classification. First, the concept of "suspiciousness" is introduced, and its size is allocated according to the number of secondary users contained in each category after each training. In order to eliminate the defects of the traditional SOM neural network, the present invention further proposes the concept of "average suspiciousness". The specific steps include: obtaining the energy matrix, using the SOM neural network algorithm to train the energy matrix to obtain the classification matrix, calculating the "suspiciousness" of each secondary user, constructing the index matrix and repeating the training process, and calculating the "suspicious degree" obtained each time degree", that is, the "average suspicious degree", and use the "average suspicious degree" to classify secondary users to identify malicious users or normal users.

Description

technical field [0001] The invention relates to a technology for responding to malicious attacks based on self-organizing map neural networks, and belongs to the field of cognitive radio technology. Background technique [0002] The rapid development of wireless communication technology has directly led to the scarcity of spectrum resources. In order to solve this problem, collaborative spectrum sensing came into being. A common centric cooperative spectrum sensing model includes a primary user, a fusion center and multiple secondary users. When the primary user is idle, the secondary user is allowed to access the primary user. The specific process of perception is: [0003] (1) After completing the detection, all secondary users send reports to the fusion center for judgment. [0004] (2) The fusion center fuses the received data and compares the fusion result with the threshold value. If the fusion result is greater than the threshold value, the judgment result is that...

AI Technical Summary

Problems solved by technology

In comparison, the physical realization of the hard decision system is higher, but the quantization process leads to a certain degree of data loss, which has a certain impact on the accuracy of the decision result

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