Machine learning-based physical layer channel authentication method

A physical layer channel and machine learning technology, which is applied in the direction of instruments, computer components, character and pattern recognition, etc., can solve the problems that affect the authentication accuracy and the threshold value is difficult to determine

Active Publication Date: 2018-07-20
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Claims
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Problems solved by technology

However, T A presents a non-random distribution, the threshold η

Method used

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  • Machine learning-based physical layer channel authentication method
  • Machine learning-based physical layer channel authentication method
  • Machine learning-based physical layer channel authentication method

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Embodiment Construction

[0063] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0064] The present invention is implemented in an actual environment with a plurality of illegal nodes and legal nodes. First, the receiver B collects channel information (CSI) of information packets from the legal information sender A and the simulated illegal information sender E, so as to ensure the The maximum time difference between is within the coherence time, and the generated classifier takes the AdaBoost classifier as an example.

[0065] Such as figure 1 Shown, a kind of physical layer channel authentication method based on machine learning comprises the following steps:

[0066] S1. Receiver B carries out channel information (CSI) collection of information packet to legal information sender A and simulation illegal information ...

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Abstract

The invention discloses a physical layer channel authentication method based on machine learning. The method comprises the steps of S1, collecting the information packet channel information (CSI) of alegal information sender A and a simulated illegal information sender E by means of a receiver B; S2, pre-processing the information packet channel information of the legal information sender A and the simulated illegal information sender E by the receiver B; S3, generating a binary training data set by the receiver B; S4, based on the classification algorithm in the machine learning algorithm, carrying out the training by the receiver B according to the binary training data set to generate a classifier; S5, judging whether a received information packet is legal or not by the receiver B by utilizing the classifier, and realizing the authentication of the information packet. The invention provides the physical layer channel authentication method based on machine learning. The method is suitable for authentication equipment and authentication scenes with limited resources. The method has the advantages of low calculation complexity and high authentication accuracy.

Description

technical field [0001] The invention relates to physical layer channel authentication, in particular to physical layer channel authentication based on machine learning. Background technique [0002] In the future wireless communication system, the number of miniature terminal devices pouring into the wireless network will increase exponentially. During the process of accessing the wireless network, the miniature terminal needs to establish security measures such as secure access authentication. However, at present, traditional access authentication is mainly based on cryptographic technology, and there are many kinds of micro terminal devices, some of which are very simple, such as wearable devices and simple Internet of Things terminals, which cannot provide complex computing resources. Edge computing nodes have With certain data processing and storage capabilities, access authentication between asymmetric resources is difficult to use traditional password-based authentica...

Claims

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Application Information

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IPC IPC(8): G06K9/62H04L29/06
CPCH04L63/126G06F18/24G06F18/214
Inventor 陈洁文红陈松林宋欢欢刘文洁
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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