KEYWORDS: Signal detection, Signal processing, Network security, Associative arrays, Information security, Education and training, Detection and tracking algorithms, Computer security, Autocorrelation, Wireless communications
Radio frequency signal information of a wireless terminal is based on inherent differences in hardware. It has uniqueness, time-invariance, independence and robustness. Therefore, it is also called the radio frequency fingerprint of the wireless device. It can be used for authentication and security access of the wireless terminal to enhance the security protection capability of wireless communication. However, the existing radio frequency fingerprint authentication technology is facing the problem of lack of corresponding identification when the radio frequency information acquisition module is linked with network management equipment. This paper studies a mapping technology between WiFi radio signals and MAC addresses based on GNU Radio stream labels, and establishes the corresponding relationship between the radio signal identification and the network layer identification by using the steps of signal collection, burst signal detection, burst location stream label creation, stream label transmission and update, and the mapping between burst signals and MAC addresses. The feasibility of this technology is verified by experiments.
Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.
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