As　the　most　popular　low-power　communication　protocol,　cybersecurity　research　on　Bluetooth　Low　Energy　(BLE)　has　garnered　significant　attention.　Due　to　BLE＇s　inherent　security　limitations　and　firmware　vulnerabilities,　spoofing　attacks　can　easily　compromise　BLE　devices　and　tamper　with　privacy　data.　In　this　paper,　we　proposed　BLEGuard,　a　hybrid　detection　mechanism　combined　cyber-physical　features　with　learning-based　techniques.　We　established　a　physical　network　testbed　to　conduct　attack　simulations　and　capture　advertising　packets.　Four　different　network　features　were　utilized　to　implement　detection　and　classification　algorithms.　Preliminary　results　have　verified　the　feasibility　of　our　proposed　methods.