Academic Journal of Computing & Information Science, 2022, 5(11); doi: 10.25236/AJCIS.2022.051103.
Dai Hang1, Jin Jing2,3
1Wuhan University of Technology, Wuhan, China
2Chn Energy Zhishen Control Technology Co. Ltd.
3Beijing Engineering Research Center of Power Station Automation, Beijing, China
The physical layer characteristics of the signal, such as electromagnetic fingerprints, will change with the time-varying channel’s variation under the influence of various factors, so that the reliability of traditional electromagnetic fingerprint authentication systems that don’t consider the dynamic changes of electromagnetic fingerprints would be reduced. In this paper, we explore and design a wireless sensor network physical layer authentication mechanism based on electromagnetic fingerprint dynamic prediction to improve the reliability of the authentication system. This system uses the LSTM network to learn and predict the dynamic of electromagnetic fingerprints affected by wireless channels and combines the unique electromagnetic fingerprint differences of legal transmitters with the channel corresponding changes to transceivers to improve the difference between the electromagnetic fingerprints from legitimate communication and the attack link, further improving the accuracy of electromagnetic fingerprint authentication. In addition, the LSTM network can also find the abnormal data and learn through continuous adaptive training to improve the detection probability.
Time-varying channel, physical layer authentication, electromagnetic fingerprint, LSTM network
Dai Hang, Jin Jing. Wireless Sensor Network Physical Layer Authentication Technology Based on Dynamic Prediction of Electromagnetic Fingerprints. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 11: 18-27. https://doi.org/10.25236/AJCIS.2022.051103.
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