Authenticating On-Body IoT Devices: An Adversarial Learning Approach

TL;DR

This paper presents a novel adversarial learning-based authentication system for on-body IoT devices that combines wireless PHY signatures with protocols, achieving high accuracy and robustness against environment and motion variations.

Contribution

It introduces a general authentication framework using adversarial neural networks and PHY signatures, overcoming limitations of existing methods that rely on specific sensors or motions.

Findings

Achieves 91.6% average authentication accuracy.

High AUROC of 0.96 indicating strong detection performance.

Outperforms conventional non-adversarial approaches.

Abstract

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.