# Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features

## TL;DR

This paper introduces a deep learning-based passive authentication system for smartphones that verifies users unobtrusively using sensor data, achieving high accuracy within seconds without explicit login actions.

## Contribution

It presents a Siamese LSTM architecture for passive user verification using multi-modal sensor data, a novel approach in smartphone authentication.

## Key findings

- 97.15% true positive rate within 3 seconds
- Achieves 0.1% false accept rate
- Uses 8 sensor modalities for verification

## Abstract

Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by frequently and unobtrusively monitoring the user's interaction with the device. In this paper, we propose a Siamese Long Short-Term Memory network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. We acquired a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users. We evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that, within 3 seconds, a genuine user can be correctly verified 97.15% of the time at a false accept rate of 0.1%.