Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

Janggoon Lee, Chanhee Park and Heejun Roh
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Janggoon Lee: Cyber Security Major, Division of Applied Mathematical Sciences, Korea University Sejong Campus, Sejong 30019, Korea
Chanhee Park: Cyber Security Major, Division of Applied Mathematical Sciences, Korea University Sejong Campus, Sejong 30019, Korea
Heejun Roh: Cyber Security Major, Division of Applied Mathematical Sciences, Korea University Sejong Campus, Sejong 30019, Korea

Energies, 2021, vol. 14, issue 4, 1-12

Abstract: Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

Keywords: bluetooth; frequency hopping; adaptive frequency hopping; spectrum sensing; wireless security (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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