# Towards Motion Invariant Authentication for On-Body IoT Devices

## TL;DR

This paper presents a novel wireless signal-based authentication method for on-body IoT devices that is robust against user motion and environment changes, enhancing security without additional sensors.

## Contribution

It introduces an adversarial multi-player network leveraging RSS features for motion-invariant device authentication, surpassing traditional sensor-dependent methods.

## Key findings

- Achieves 90.4% average authentication accuracy.
- Attains an AUROC of 0.958, indicating high detection performance.
- Demonstrates robustness across static and dynamic body motions.

## Abstract

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.