| Title | Improving Wireless Network Security Based On Radio Fingerprinting |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lin, Yun, Chang, Jie |
| Conference Name | 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
| Keywords | authorisation, Communication system security, composability, feature extraction, Fourier transforms, fractional fourier transform, Human Behavior, identity authentication, Metrics, physical characteristics, pubcrawl, radio fingerprinting, radio frequency fingerprint extraction, Radio Frequency Fingerprints, radio transmitters, Resiliency, security, security threats, sensor security, Signal to noise ratio, telecommunication security, Transient analysis, transient signals, wireless device transmitters, wireless devices, Wireless Network Security, wireless networks, Wireless sensor networks |
| Abstract | With the rapid development of the popularity of wireless networks, there are also increasing security threats that follow, and wireless network security issues are becoming increasingly important. Radio frequency fingerprints generated by device tolerance in wireless device transmitters have physical characteristics that are difficult to clone, and can be used for identity authentication of wireless devices. In this paper, we propose a radio frequency fingerprint extraction method based on fractional Fourier transform for transient signals. After getting the features of the signal, we use RPCA to reduce the dimension of the features, and then use KNN to classify them. The results show that when the SNR is 20dB, the recognition rate of this method is close to 100%. |
| DOI | 10.1109/QRS-C.2019.00076 |
| Citation Key | lin_improving_2019 |
Improving Wireless Network Security Based On Radio Fingerprinting