The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through
Federated Data-Augmented Radio Fingerprinting
- URL: http://arxiv.org/abs/2105.03671v1
- Date: Sat, 8 May 2021 10:48:02 GMT
- Title: The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through
Federated Data-Augmented Radio Fingerprinting
- Authors: Mauro Piva, Gaia Maselli, Francesco Restuccia
- Abstract summary: We propose a novel training framework based on federated machine learning (FML) and data augmentation (DAG) to boost the accuracy of RFID clone detection.
To the best of our knowledge, this is the first paper experimentally demonstrating the efficacy of FML and DA on a large device population.
- Score: 11.03108444237374
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Millions of RFID tags are pervasively used all around the globe to
inexpensively identify a wide variety of everyday-use objects. One of the key
issues of RFID is that tags cannot use energy-hungry cryptography. For this
reason, radio fingerprinting (RFP) is a compelling approach that leverages the
unique imperfections in the tag's wireless circuitry to achieve large-scale
RFID clone detection. Recent work, however, has unveiled that time-varying
channel conditions can significantly decrease the accuracy of the RFP process.
We propose the first large-scale investigation into RFP of RFID tags with
dynamic channel conditions. Specifically, we perform a massive data collection
campaign on a testbed composed by 200 off-the-shelf identical RFID tags and a
software-defined radio (SDR) tag reader. We collect data with different
tag-reader distances in an over-the-air configuration. To emulate implanted
RFID tags, we also collect data with two different kinds of porcine meat
inserted between the tag and the reader. We use this rich dataset to train and
test several convolutional neural network (CNN)--based classifiers in a variety
of channel conditions. Our investigation reveals that training and testing on
different channel conditions drastically degrades the classifier's accuracy.
For this reason, we propose a novel training framework based on federated
machine learning (FML) and data augmentation (DAG) to boost the accuracy.
Extensive experimental results indicate that (i) our FML approach improves
accuracy by up to 48%; (ii) our DA approach improves the FML performance by up
to 31%. To the best of our knowledge, this is the first paper experimentally
demonstrating the efficacy of FML and DA on a large device population. We are
sharing with the research community our fully-labeled 200-GB RFID waveform
dataset, the entirety of our code and trained models.
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