Stacking Framework for Detecting Braktooth Attack on IoT Health Care Systems

The rise in Internet of Things (IoT) devices, particularly in healthcare systems, has brought about significant security concerns, especially with the widespread use of Bluetooth technology. The discovery of BrakTooth vulnerabilities in the Bluetooth Classic (BT Classic) stack further amplifies these risks, potentially compromising sensitive medical data and patient safety. In light of this, the present study proposes a unique framework for detecting BrakTooth attacks on IoT healthcare systems. By utilizing the stacking ensemble algorithm and the ISOT BrakTooth dataset, the framework aims to identify anomalies related to BrakTooth in Bluetooth traffic at the Link Manager Protocol (LMP) layer. The framework's effectiveness is demonstrated through extensive experimentation, achieving an accuracy rate of 94.01%. The results highlight the system's capability to proactively detect and mitigate BrakTooth vulnerabilities, bolstering cybersecurity resilience in interconnected healthcare environments.