一种基于CNN的无线路由器指纹识别方法

A wireless router fingerprint identification method based on CNN

针对无线设备“指纹”特征提取技术含量较高,且技术手段较为复杂的问题,在无线空间信道状态不变的前提下,提出了一种基于卷积神经网络(convolution neural network,CNN)自动分类无线路由器指纹的识别方法,解决无线设备“指纹”特征提取困难的问题.文章设计并实现了一种通过接收处理多输入多输出(multiple-input multiple-output,MIMO)信号幅度识别无线路由器的方法,该方法通过采集无线路由器的信道状态信息(channel state information,CSI),并对CSI的幅度数据进行预先平滑和去噪处理,然后把预处理后的幅度数据作为设备的指纹特征,最后通过机器学习的算法进行分类和识别.实验采用CNN对10台商用无线路由器进行分类和识别,准确率达到96%以上,证明了使用CSI来识别无线路由器是可行的.

Aiming at the problem of high technical content and complex technical means of fingerprint feature extraction of wireless devices,under the premise of constant wireless space channel state,a method based on convolution neural network(CNN)for automatic classification of wireless routers is presented to solve the difficult problem of fingerprint extraction.The main work of the paper is to design and implement a method for identifying wireless routers through receiving and processing multiple-input multiple-output(MIMO)signal amplitudes.This method collects channel state information of wireless routers,pre-smooths and denoises the amplitude data of channel state information,and then use the pre-processed amplitude data as the fingerprint feature of the device,and finally is classified and identified by the machine learning algorithm.The experiment used CNN to classify and identify 10 commercial wireless routers with an accuracy rate of over 96%,and proved that using CSI to identify wireless routers is feasible.