Poster: BLE-Shield: A Hybrid Framework for Threat Identification in Bluetooth Low Energy (BLE) Networks

Bluetooth Low Energy (BLE) is increasingly prevalent in IoT networks, particularly in high-risk environments such as medical IoT. However, existing security research in BLE often focuses on isolated device types or controlled environments, overlooking the complexities of real-world BLE interactions, including environmental noise and the heterogeneous nature of devices. This paper proposes a multilayered framework for BLE data extraction designed to capture, process, and correlate data from diverse sources to construct comprehensive behavioral signatures. Our approach explores traffic analysis and introduces the potential of spectrum analysis to identify malicious activity beyond conventional packet-level monitoring. By identifying key behavioral markers and deviations from expected norms, this approach enhances BLE anomaly detection and strengthens the security of IoT networks.