Physical-Layer Channel Authentication for 5G via Machine Learning Algorithm. (2nd October 2018)

• Record Type: Journal Article
• Authors: Chen, Songlin; Wen, Hong; Wu, Jinsong; Chen, Jie; Liu, Wenjie; Hu, Lin; Chen, Yi
• Other Names: Casares-Giner Vicente Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2018(2018)
• Publication Date: 2018-10-02
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2018/6039878
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : 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.