Northeastern University
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Abstract: In this thesis, we describe a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques. Our approach of radio fingerprinting applies ML over raw I/Q samples without specifically selecting features of interest. It distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing...

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