  By adding users as a new dimension to connectivity, on-body
Internet-of-Things (IoT) devices have gained considerable momentum in recent
years, while raising serious privacy and safety issues. Existing approaches to
authenticate these devices limit themselves to dedicated sensors or specified
user motions, undermining their widespread acceptance. This paper overcomes
these limitations with a general authentication solution by integrating
wireless physical layer (PHY) signatures with upper-layer protocols. The key
enabling techniques are constructing representative radio propagation profiles
from received signals, and developing an adversarial multi-player neural
network to accurately recognize underlying radio propagation patterns and
facilitate on-body device authentication. Once hearing a suspicious
transmission, our system triggers a PHY-based challenge-response protocol to
defend in depth against active attacks. We prove that at equilibrium, our
adversarial model can extract all information about propagation patterns and
eliminate any irrelevant information caused by motion variances and environment
changes. We build a prototype of our system using Universal Software Radio
Peripheral (USRP) devices and conduct extensive experiments with various static
and dynamic body motions in typical indoor and outdoor environments. The
experimental results show that our system achieves an average authentication
accuracy of 91.6%, with a high area under the receiver operating characteristic
curve (AUROC) of 0.96 and a better generalization performance compared with the
conventional non-adversarial approach.
