无线局域网由于其开放的信道环境和传统的密钥身份验证机制, 安全问题十分严峻。通过射频指纹识别技术, 提取无线设备硬件特征进行身份验证, 能够大大提高无线网络安全性。本文基于通用软件无线电外设(USRP)和GNU Radio开源平台, 提取IEEE 802.11a/g信号载波频偏作为指纹, 结合神经网络分类器进行识别。首先接收信号并提取每帧信号载波频偏, 然后训练神经网络分类器, 最后利用此分类器对无线设备进行识别。在办公室和体育馆2种典型室内环境进行无线设备个体识别实验, 识别率均大于90%。实验结果说明, 基于软件无线电提取信号载波频偏可以识别出不同的无线设备, 检测出非法设备接入, 能够提高无线网络安全性。

There are risks in Wireless Local Area Network(WLAN) because of the open channel environment and the traditional key authentication mechanism. Radio frequency fingerprinting identification which extracts hardware features of wireless devices for authentication, could greatly improve the wireless network security. Based on Universal Software Radio Peripheral(USRP) and GNU Radio open source platform, carrier frequency offset of IEEE 802.11a/g signals is extracted as the fingerprint, and the neural network classifier is used for recognition. Firstly, this method collects IEEE 802.11 a/g signals and extracts the carrier frequency offset of each frame, then trains a neural network classifier. Lastly it identifies wireless devices by using the classifier. In two typical indoor environments of the office and the gymnasium, the recognition rate of wireless devices is more than 90%. The experimental results show that wireless devices can be identified by extracting carrier frequency offset of signals based on software radio, and illegal device access can be detected, which could improve the security of wireless network.