Radio frequency fingerprinting identification for Zigbee via lightweight CNN
| Autor: | Tingping Zhang, Wang Huifang, Guangwei Qing |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry 010102 general mathematics Smart device 020206 networking & telecommunications Cryptography 02 engineering and technology 01 natural sciences Convolutional neural network law.invention Identification (information) law Power consumption Home automation Embedded system 0202 electrical engineering electronic engineering information engineering Radio frequency 0101 mathematics Electrical and Electronic Engineering Communications protocol business |
| Zdroj: | Physical Communication. 44:101250 |
| ISSN: | 1874-4907 |
| DOI: | 10.1016/j.phycom.2020.101250 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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