AVOIDING SECURITY ATTACKS IN PROXIMITY REPORTING AND CONTACT TRACING USING BLE ANALYTICS IN AN INTERNET-OF-THINGS FRAMEWORK

In connection with the ongoing COVID-19 pandemic, contact tracing mobile applications (e.g., that leverage Wi-Fi and Bluetooth™ Low Energy (BLE)) are being deployed. Many of those applications are susceptible to different security attacks. To address those types of challenges, techniques are presented herein that provide a multi-tiered mechanism for an optimized genuine client neighbor filtering technique that, among other things, may detect, score, and filter fake BLE advertisement attacks to improve the efficacy of the overall contact tracing application ecosystem and reduce airtime utilization from fake advertisements in the industrial, scientific and medical (ISM) frequency bands. For example, a man-in-the-middle (MITM) or other adversary may attach a mobile device to a carrier (such as a dog, car, flying drone, etc.) that advertises proximity identifiers to the devices of unsuspecting people. Additionally, the presented techniques address device power and storage drain that are associated with denial-of-service attacks involving a large volume of messages