In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.


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    Title :

    Hypersphere Projection-Guided Radio Frequency Fingerprinting Authentication in the Open World


    Contributors:
    Fu, Xue (author) / Wang, Yu (author) / Lin, Yun (author) / Zhang, Qianyun (author) / Gui, Guan (author) / Ohtsuki, Tomoaki (author) / Sari, Hikmet (author)


    Publication date :

    2024-06-24


    Size :

    2040683 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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