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

Abstract EN

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.