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Radio frequency fingerprinting identification for Zigbee via lightweight CNN
Physical Communication ( IF 2.2 ) Pub Date : 2020-11-28 , DOI: 10.1016/j.phycom.2020.101250
Guangwei Qing , Huifang Wang , Tingping Zhang

Zigbee is a popular communication protocol in the Internet of things (IoT) which shows great potential in smart home. However, the smart device has the risk of being hijacked by unauthorized users and may result in privacy disclosure. Traditional device identification is based on cryptography which is easy to be cracked. Recently, radio frequency fingerprinting identification (RFFID) is popular in device identification. Traditional RFFID’s power consumption and cost is unacceptable to Zigbee. In order to reduce the cost, more effective model can be used to reduce the number of neurons. This paper proposes a RFFID method based on lightweight convolution neural network (CNN) which can adopt low power consumption and cost. The simulation result shows that this method can identification Zigbee device, and the accuracy reached 100%. Also, the parameter has reduced to about 93%.



中文翻译:

通过轻量级CNN对Zigbee进行射频指纹识别

Zigbee是物联网(IoT)中流行的通信协议,在智能家居中显示出巨大的潜力。但是,智能设备具有被未授权用户劫持的风险,并可能导致隐私泄露。传统的设备识别基于易于破解的密码学。最近,射频指纹识别(RFFID)在设备识别中很流行。Zigbee不能接受传统RFFID的功耗和成本。为了降低成本,可以使用更有效的模型来减少神经元的数量。提出了一种基于轻量级卷积神经网络(CNN)的RFFID方法,该方法可以降低功耗和成本。仿真结果表明,该方法可以识别Zigbee设备,精度达到100%。也,

更新日期:2020-12-01
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