  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.
