Towards Markov Synthetic BLE Packet Generation for IoT Systems

Bluetooth Low Energy (BLE) is widely used in devices like smartphones and personal trackers, but also raises serious privacy risks, especially related to stalking. Machine Learning (ML)-based methods for detecting BLE trackers across vendors show promise, yet are limited by the scarcity and variability of BLE advertisement packets, which hinders model performance. This paper addresses this limitation by introducing the first publicly available, open-source tool for generating synthetic BLE advertisement packets using a Markov model. Designed for structured time-series data, the model can produce all valid BLE packet permutations, addressing a key data gap for research and training. As a case study, synthetic Samsung SmartTag (nearby) packets are used to augment training data, resulting in a 37% increase in median prediction confidence level in real-world evaluations.