現今感知無線電(cognitive radio, CR)被認為是解決頻譜使用效率不佳的重要技術，然而此技術也衍伸出值得探討的安全性問題，其中較為人所研究的攻擊是模仿主要使用者攻擊(primary user emulation attack, PUEA)，本論文提出利用無線通道的統計特性辨識模仿主要使用者攻擊。在無線環境下，不同的接收端與傳送端之間的通道統計特性都是獨一無二的，因此可以將此特性當作是一種無線電指紋(radio fingerprint)來使用，藉由此方法可以在實體層先行認證，進而減少認證時間。此外論文提出利用感知無線電的跨層級智慧學習能力，使高準確性的上層認證建立出通道統計特性的資料庫，幫助實體層認證。然而實體層認證雖可以快速辨識傳送端身分，但考慮某些情況下單一節點偵測可能受到許多因素影響偵測結果，這裡提出合作式偵測，並分析合作式偵測效能。模擬結果驗證本論文所提出方法的可行性。

Cognitive radio (CR) today refers to a relatively wide range of technologies for making wireless systems computationally intelligent, with a view to improving the spectral utilization. However, CR also raises new security issues, the most of which concerns primary user emulation attack (PUEA). This work proposes a method by using the characteristics of wireless channels to identify the PUEA. In a wireless environment, the statistical properties of the wireless channel between the receiver and transmitter are unique. They can therefore be utilized as a radio fingerprint to authenticate directly users via physical (PHY) layers, thereby improving detection time. Before the PHY layer detection is introduced, the cross-layer intelligent learning ability of CR is exploited to establish a detection database by utilizing the accuracy of higher layer authentications. Although the transmitter can be rapidly identified using the proposed detection mechanism, several factors may affect the result of detection by a single node. Therefore, the cooperative detection scheme is adopted herein to improve the detection performance. The detection probability is analyzed. Simulation results confirm the advantages of the proposed schemes.

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