Graduate Thesis Or Dissertation
 

On the domain generalizability of RF fingerprints through multifractal dimension representation

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rb68xm31j

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  • RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data from one domain performs badly when tested on data from a different domain. Some examples of a domain change include varying the location or environment of the device and varying the time or day of data collection for the device. This work proposes using multifractal analysis and the variance fractal dimension trajectory (VFDT) as a data representation input to the deep neural network to extract device fingerprints that are domain generalizable. This work analyzes the effectiveness of the VFDT in detecting device-specific signatures via hardware-impaired signal distortions, and evaluates its robustness in real-world settings, using an experimental testbed of WiFi-enabled devices under different locations and at different scales. The results of the evaluation show that the VFDT yields a scalable, more robust and generalizable model than when using the raw transmitted IQ data.
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  • Pending Publication
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  • 2023-08-02 to 2023-08-22

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