![](bg6.png)
an active sniffer to capture normal and attack traffic,
using the InternalBlue framework and a Broadcom
BCM4339 Bluetooth Controller. Finally, we used the
collected data to train various machine-learning mod-
els to classify attack data and achieved good perfor-
mance with the Random Forest model. Our inexpen-
sive and simple detection setup can also be extended
to notify various stakeholders, such as caretakers and
family members as soon as an attack is detected, thus
ensuring the safety and welfare of the senior resi-
dents in a smart home. In the future, we intend to
enhance our current low-cost setup to capture addi-
tional features such as RF signal strength, RF sig-
nal frequency offset, bit error rate, invalid data rate,
and more to model and identify a broader range of
Bluetooth attacks. Further, we also plan to integrate
Ubertooth One, to extend the existing setup to detect
SweynTooth-based (Garbelini et al., 2020) attacks on
BLE devices.
ACKNOWLEDGEMENTS
This project was supported in part by collaborative
research funding from the National Research Council
of Canada’s Aging in Place Program.
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