Revisiting Bluetooth Adaptive Frequency Hopping Prediction with a Ubertooth
Janggoon Lee, Chanhee Park, Heejun Roh · 2021
Due to frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices is a challenging problem. To this end, a state-of-the-art low-cost sniffing system employing two cheap Ubertooth devices [1], proposes machine learning-based prediction technique for adaptive frequency hopping (AFH) map by collecting packet statistics and spectrum sensing. In this paper, we revisit the AFH prediction problem. Our intention of this approach is that proposing better way to label data set to train Support Vector Machine (SVM) that could be done without measuring packet rates by visiting all 79 channels. We build a prototype of AFH prediction technique with a Ubertooth and a SVM. Our result shows that high accuracy can be achieved without the packet-based classifier of BlueEar.