Differential Contour Stellar-Based Radio Frequency Fingerprint Identification for Internet of Things

IEEE Access

Bin Zhang¹  Jingchao Li²  Chunlei Ji²  Yulong Ying³ 

[1] Department of Mechanical Engineering, Kanagawa University, Yokohama, Japan;School of Electronic and Information, Shanghai Dianji University, Shanghai, China;School of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, China;

关键词: Differential contour stellar;    radio frequency fingerprint;    physical layer authentication;    fine portrait;    deep convolutional neural network;   

DOI  :  10.1109/ACCESS.2021.3071352

来源: DOAJ

【 摘 要 】

Data attacks from illegal access devices of the Internet of Things will cause serious interference and threats to the entire network. It is difficult to ensure the security of the communication system only by relying on traditional application layer password authentication methods. Therefore, it is of great significance to design an effective physical layer authentication system based on radio frequency fingerprints. Regarding the issue above, this paper proposes a novel physical layer authentication method for Internet of Things based on differential contour stellar. Through the test of identification and authentication of 20 WiFi network card devices from same manufacturer, same type and same batch, the recognition accuracy rate can reach 98.6% by the proposed method. The proposed method can improve the effect of radio frequency fingerprint identification from three aspects: i. The differential processing can effectively reduce the negative influence of phase rotation caused by carrier frequency offset and Doppler effects; ii. The color processing can effectively reduce the negative influence of random noise caused by channel noise; iii. It is suitable for processing large-scale networks and the massive data they bring.

【 授权许可】

Unknown