Self-organization mapping (SOM) neural network-based malicious user discrimination method in cognitive radio

Abstract

The present invention discloses an SOM neural network-based malicious user discrimination method in cognitive radio. The method of the present invention utilizes an SOM neural network to learn the distribution characteristics of an input energy matrix, effectively classifies the input according to the learning results, and comprises the step of firstly introducing a suspicious degree concept, wherein the suspicious degree is distributed according to the number of the secondary users of each type after every time training. In order to eliminate the defects of the conventional SOM neural network, the present invention further provides an average suspicious degree concept. The concrete steps comprise obtaining an energy matrix, utilizing an SOM neural network algorithm to train the energy matrix to obtain a classification matrix, calculating the suspicious degree of each secondary user, constructing an index matrix and repeating the training process, averaging the suspicious degrees obtained at each time, namely obtaining the average suspicious degree, utilizing the average suspicious degree to classify the secondary users, and identifying the malicious users or the 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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