# Implicit Sensor-based Authentication of Smartphone Users with Smartwatch

## TL;DR

This paper introduces iAuth, a continuous implicit authentication system using sensor data and machine learning to verify smartphone users, enhancing security without noticeable performance impact.

## Contribution

The paper presents a novel sensor-based authentication system that leverages existing smartphone sensors and machine learning for continuous user verification.

## Key findings

- Achieves 92.1% authentication accuracy
- Consumes less than 2% battery
- Operates with negligible system overhead

## Abstract

Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial log-in mechanism, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous authentication of the end-user based on his or her behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We design a system that gives accurate authentication using machine learning and sensor data from multiple mobile devices. Our system can achieve 92.1% authentication accuracy with negligible system overhead and less than 2% battery consumption.