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MAC ID Spoofing-Resistant Radio Fingerprinting

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Citation Author(s):
Tong Jian, Bruno Costa Rendon, Andrey Gritsenko, Jennifer Dy, Kaushik Chowdhury, Stratis Ioannidis
Submitted by:
Tong Jian
Last updated:
11 November 2019 - 9:13pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Tong Jian

Abstract 

Abstract: 

We explore the resistance of deep learning methods for radio fingerprinting to MAC ID spoofing. We demonstrate that classifying transmission slices enables classification of a transmission with a fixed-length input deep classifier, enhances shift-invariance, and, most importantly, makes the classifier resistant to MAC ID spoofing. This is a consequence of the fact that the classifier does not learn to use the MAC ID to classifying among transmissions, but relies on other inherent discriminating signals, e.g., device imperfections. We demonstrate this via experiments on transmissions generated using two protocols, namely, WiFi and ADS-B.

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Dataset Files

MACSpoof_Globalsip2019

(156)