Deep learning-based device fingerprinting has recently
been recognized as a key enabler for automated network access
authentication. Its robustness to impersonation attacks due to the
inherent difficulty of replicating physical features is what distinguishes
it from conventional cryptographic solutions. Although
device fingerprinting has shown promising performances, its
sensitivity to changes in the network operating environment still
poses a major limitation. This paper presents an experimental
framework that aims to study and overcome the sensitivity
of LoRa-enabled device fingerprinting to such changes. We
first begin by describing RF datasets we collected using our
LoRa-enabled wireless device testbed. We then propose a new
fingerprinting technique that exploits out-of-band distortion
information caused by hardware impairments to increase the
fingerprinting accuracy. Finally, we experimentally study and
analyze the sensitivity of LoRa RF fingerprinting to various
network setting changes. Our results show that fingerprinting
does relatively well when the learning models are trained and
tested under the same settings. However, when trained and tested
under different settings, these models exhibit moderate sensitivity
to channel condition changes and severe sensitivity to protocol
configuration and receiver hardware changes when IQ data is
used as input. However, with FFT data is used as input, they
perform poorly under any change.
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Leveraging MIMO Transmit Diversity for Channel-Agnostic Device Identification
The accurate identification of wireless devices is
critical for enabling automated network access monitoring and
authenticated data communication in large-scale networks; e.g.,
IoT networks. RF fingerprinting has emerged as a potential solution
for device identification by leveraging the transmitter unique
manufacturing impairments of the RF components. Although
deep learning is proven efficient in classifying devices based
on the hardware impairments, trained models perform poorly
due to channel variations. That is, although training and testing
neural networks using data generated during the same period
achieve reliable classification, testing them on data generated at
different times degrades the accuracy substantially. To the best
of our knowledge, we are the first to propose to leverage MIMO
capabilities to mitigate the channel effect and provide a channelresilient
device classification. For the proposed technique we
show that, for Rayleigh channels, blind partial channel estimation
enabled by MIMO increases the testing accuracy by up to 40%
when the models are trained and tested over the same channel,
and by up to 60% when the models are tested on a channel that
is different from that used for training.
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- Award ID(s):
- 2003273
- NSF-PAR ID:
- 10330001
- Date Published:
- Journal Name:
- IEEE International Conference on Communications
- ISSN:
- 1938-1883
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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