20 research outputs found
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth
A scalable and computationally efficient framework is designed to fingerprint
real-world Bluetooth devices. We propose an embedding-assisted attentional
framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its
generalization capability is analyzed in different settings and the effect of
sample length and anti-aliasing decimation is demonstrated. The embedding
module serves as a dimensionality reduction unit that maps the high dimensional
3D input tensor to a 1D feature vector for further processing by the ATN
module. Furthermore, unlike the prior research in this field, we closely
evaluate the complexity of the model and test its fingerprinting capability
with real-world Bluetooth dataset collected under a different time frame and
experimental setting while being trained on another. Our study reveals a 9.17x
and 65.2x lesser memory usage at a sample length of 100 kS when compared to the
benchmark - GRU and Oracle models respectively. Further, the proposed Mbed-ATN
showcases 16.9x fewer FLOPs and 7.5x lesser trainable parameters when compared
to Oracle. Finally, we show that when subject to anti-aliasing decimation and
at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework
results in a 5.32x higher TPR, 37.9% fewer false alarms, and 6.74x higher
accuracy under the challenging real-world setting.Comment: To Appear in IEEE Transactions on Cognitive Communications and
Networkin
Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices
RF fingerprinting is emerging as a physical layer security scheme to identify
illegitimate and/or unauthorized emitters sharing the RF spectrum. However, due
to the lack of publicly accessible real-world datasets, most research focuses
on generating synthetic waveforms with software-defined radios (SDRs) which are
not suited for practical deployment settings. On other hand, the limited
datasets that are available focus only on chipsets that generate only one kind
of waveform. Commercial off-the-shelf (COTS) combo chipsets that support two
wireless standards (for example WiFi and Bluetooth) over a shared dual-band
antenna such as those found in laptops, adapters, wireless chargers, Raspberry
Pis, among others are becoming ubiquitous in the IoT realm. Hence, to keep up
with the modern IoT environment, there is a pressing need for real-world open
datasets capturing emissions from these combo chipsets transmitting
heterogeneous communication protocols. To this end, we capture the first known
emissions from the COTS IoT chipsets transmitting WiFi and Bluetooth under two
different time frames. The different time frames are essential to rigorously
evaluate the generalization capability of the models. To ensure widespread use,
each capture within the comprehensive 72 GB dataset is long enough (40
MSamples) to support diverse input tensor lengths and formats. Finally, the
dataset also comprises emissions at varying signal powers to account for the
feeble to high signal strength emissions as encountered in a real-world
setting.Comment: Revision Under Revie
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
A novel cross-domain attentional multi-task architecture - xDom - for robust
real-world wireless radio frequency (RF) fingerprinting is presented in this
work. To the best of our knowledge, this is the first time such comprehensive
attention mechanism is applied to solve RF fingerprinting problem. In this
paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead
of synthetic waveform generation) in a rich multipath and unavoidable
interference environment in an indoor experimental testbed. We show the impact
of the time-frame of capture by including waveforms collected over a span of
months and demonstrate the same time-frame and multiple time-frame
fingerprinting evaluations. The effectiveness of resorting to a multi-task
architecture is also experimentally proven by conducting single-task and
multi-task model analyses. Finally, we demonstrate the significant gain in
performance achieved with the proposed xDom architecture by benchmarking
against a well-known state-of-the-art model for fingerprinting. Specifically,
we report performance improvements by up to 59.3% and 4.91x under single-task
WiFi and BT fingerprinting respectively, and up to 50.5% increase in
fingerprinting accuracy under the multi-task setting.Comment: Accepted to IEEE GLOBECOM 202