Physical‐Layer Channel Authentication for 5G via Machine Learning Algorithm

By utilizing the radio channel information to detect spoofing attacks, channel based physical layer (PHY‐layer) enhanced authentication can be exploited in light‐weight securing 5G wireless communications. One major obstacle in the application of the PHY‐layer authentication is its detection rate. In this paper, a novel authentication method is developed to detect spoofing attacks without a special test threshold while a trained model is used to determine whether the user is legal or illegal. Unlike the threshold test PHY‐layer authentication method, the proposed AdaBoost based PHY‐layer authentication algorithm increases the authentication rate with one‐dimensional test statistic feature. In addition, a two‐dimensional test statistic features authentication model is presented for further improvement of detection rate. To evaluate the feasibility of our algorithm, we implement the PHY‐layer spoofing detectors in multiple‐input multiple‐output (MIMO) system over universal software radio peripherals (USRP). Extensive experiences show that the proposed methods yield the high performance without compromising the computing complexity.