BANDANA -- Body Area Network Device-to-device Authentication using Natural gAit
This addresses a challenging, unsolved problem for body area networks by enabling implicit device-to-device authentication.
The paper tackled the problem of secure spontaneous authentication between devices worn on the same body by proposing BANDANA, which uses gait acceleration patterns to extract secrets, resulting in a robust method demonstrated on two datasets with analysis of discriminability and attack scenarios.
Secure spontaneous authentication between devices worn at arbitrary location on the same body is a challenging, yet unsolved problem. We propose BANDANA, the first-ever implicit secure device-to-device authentication scheme for devices worn on the same body. Our approach leverages instantaneous variation in acceleration patterns from gait sequences to extract always-fresh secure secrets. It enables secure spontaneous pairing of devices worn on the same body or interacted with. The method is robust against noise in sensor readings and active attackers. We demonstrate the robustness of BANDANA on two gait datasets and discuss the discriminability of intra- and inter-body cases, robustness to statistical bias, as well as possible attack scenarios.