Classifying WiFi "physical fingerprints" using complex deep learning

Logan Smith; Nicholas Smith; Joshua Hopkins; Daniel Rayborn; John E. Ball; Bo Tang; Maxwell Young

doi:10.1117/12.2557933

24 April 2020 Classifying WiFi "physical fingerprints" using complex deep learning

Logan Smith, Nicholas Smith, Joshua Hopkins, Daniel Rayborn, John E. Ball, Bo Tang, Maxwell Young

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940J (2020) https://doi.org/10.1117/12.2557933
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Wireless communication is susceptible to security breaches by adversarial actors mimicking Media Access Controller (MAC) addresses of currently-connected devices. Classifying devices by their “physical fingerprint” can help to prevent this problem since the fingerprint is unique for each device and independent of the MAC address. Previous techniques have mapped the WiFi signal to real values and used classification methods that support solely real-valued inputs. In this paper, we put forth four new deep neural networks (NNs) for classifying WiFi physical fingerprints: a real-valued deep NN, a corresponding complex-valued deep NN, a real-valued deep CNN, and the corresponding complex-valued deep convolutional NN (CNN). Results show state-of-the-art performance against a dataset of nine WiFi devices.

Conference Presentation

Citation Download Citation

Logan Smith, Nicholas Smith, Joshua Hopkins, Daniel Rayborn, John E. Ball, Bo Tang, and Maxwell Young "Classifying WiFi "physical fingerprints" using complex deep learning", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940J (24 April 2020); https://doi.org/10.1117/12.2557933

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