A Closer Look at Mobile App Usage as a Persistent Biometric: A Small Case Study

TL;DR

This study investigates the potential of using mobile app usage patterns as a persistent behavioral biometric by representing usage data as images and training a deep learning classifier, achieving high accuracy without template updates.

Contribution

It introduces a novel approach of feeding app usage images in random order to demonstrate the persistence of certain behavioral biometric features.

Findings

Achieved 96.8% F-score in user classification

Demonstrated persistence of app usage patterns over time

Proposed a new image-based representation of app data

Abstract

In this paper, we explore mobile app use as a behavioral biometric identifier. While several efforts have also taken on this challenge, many have alluded to the inconsistency in human behavior, resulting in updating the biometric template frequently and periodically. Here, we represent app usage as simple images wherein each pixel value provides some information about the user's app usage. Then, we feed use these images to train a deep learning network (convolutional neural net) to classify the user's identity. Our contribution lies in the random order in which the images are fed to the classifier, thereby presenting novel evidence that there are some aspects of app usage that are indeed persistent. Our results yield a 96.8% $F$-score without any updates to the template data.