A Closer Look at Mobile App Usage as a Persistent Biometric: A Small Case Study
This addresses the challenge of inconsistent human behavior in biometrics for mobile security applications, though it is incremental as it builds on prior efforts with a novel training approach.
The paper tackled the problem of using mobile app usage as a persistent behavioral biometric identifier by representing app usage as images and training a convolutional neural network to classify user identity, achieving a 96.8% F-score without template updates.
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.