PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within Ultra Reliable Low Latency Communication (URLLC) scenarios in order to meet the requirement of secure user data transmissions in the sense of authenticity and integrity. In this work, we investigate the performance of supervised learning classifiers for discriminating legitimate transmitters from illegimate ones in such scenarios. We further present our methodology of data collection using Software Defined Radio (SDR) platforms and the data processing pipeline including e.g. necessary preprocessing steps. Finally, the performance of the considered supervised learning schemes under different side conditions is presented.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Supervised Learning for Physical Layer Based Message Authentication in URLLC Scenarios


    Contributors:


    Publication date :

    2019-09-01


    Size :

    258463 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    URLLC Physical Layer Authentication based on non-linear Supervised Learning

    Weinand, Andreas / Lipps, Christoph / Karrenbauer, Michael et al. | IEEE | 2023



    Learning Enabled Adaptive Multiple Attribute-based Physical Layer Authentication

    Fang, Xiaojie / Yin, Xinyu / Mei, Lin et al. | IEEE | 2020


    Physical Layer Authentication in Private Campus Networks based on Machine Learning

    Kuruvatti, Nandish P. / Mallikarjun, Sachinkumar B. / Kusumapani, Sai Charan et al. | IEEE | 2023