Physical layer spoofing detection based on sparse signal processing and fuzzy recognition

Abstract

Spoofing attacks is one of the most critical attacks in wireless communication security. Traditional solutions are based on cryptology which is performed in the upper layers, and face many challenges especially in resource-limited application. To overcome this hurdle, physical-layer security has been received a lot of attention recently. In this study, the authors propose a physical-layer spoofing detecting scheme, where signal processing and feature recognition are utilised to improve the detection performance. In this study, they present a pretreatment process based on sparse representation (SR) to reinforce the characteristic of the signal. Furthermore, they formulate the problem of spoofing detection as one of the feature extraction and recognition, and employ a developed fuzzy C-mean algorithm to further increase the recognition accuracy. In addition, in order to verify the proposed method, they conduct experiments and use numerical simulation and analysis to evaluate the detection performance. Results showed that the proposed approach can improve the recognition accuracy significantly (increased by one order of magnitude) and the complexity is acceptable (polynomial complexity). Their findings showed that combining SR and feature extraction and recognition, the proposed method provided a good access to achieve a higher accuracy scheme of spoofing detection.