Abstract
Physical layer security (PLS) is a promising technology to enhance security performance of wireless communication systems, while the analyzing of PLS abnormal user detection is an important aspect of PLS research. Considering random matrix theory (RMT) is a time-efficient and theory-mature method, we will utilize RMT to analyze the abnormal user detection problem from the perspective of physical layer data analysis, where the carrier frequency offset (CFO) data is used as an indicator for abnormal user detection. Specially, the ring law theory and empirical spectral analysis of RMT are adopted for the analysis of CFO data, which is time-efficient and can be implanted in other detection methods. The proposed abnormal user detection method can provide a guidance for the appropriate choice of security enhancement technologies, so as to improve the utilization efficiency of different PLS technologies.
Highlights
Due to the open nature of wireless channel, the physical layer of wireless communication is directly facing the security threat of abnormal users
For N = 16, the abnormal user can be detected before attack ratio is larger than 0.5, i.e., α < 0.5, since the miss detection rate is about 10−4. iv) The detection rate can be improved by increasing the number of antennas or/and by sliding the sampling time windows to accumulate more abnormal data, which can provide a flexible trade-off between the source consumptions and the detection accuracy
The advantage of this method is that the algorithm is time-efficient and theorymature, which can be implanted in other detection methods to save time and memory
Summary
Due to the open nature of wireless channel, the physical layer of wireless communication is directly facing the security threat of abnormal users. Based on the existing estimated CFO data, literature [8], [10], [11] adopt the CFO data to detect the abnormal users. Literature [11] adopts CFO information to generate secret keys at the transceiver to avoid the access of abnormal user These CFO-based detection methods do not need the practical system to make too many changes to the signal transceiver, which is conducive to the practical application and deployment. 2) The RMT is used to analyze the CFO data in different time windows, and real-time analysis of the estimated CFOs are carried out to detect abnormal user.
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