Mobile Device Identification via User Behavior Analysis

Abstract

Modern mobile devices are capable of sensing a large variety of changes, ranging from users’ motions to environmental conditions. Context-aware applications utilize the sensing capability of these devices for various purposes, such as human activity recognition, health coaching or advertising, etc. Identifying devices and authenticating unique users is another application area where mobile device sensors can be utilized to ensure more intelligent, robust and reliable systems. Traditional systems use cookies, hardware or software fingerprinting to identify a user but due to privacy and security vulnerabilities, none of these methods propose a permanent solution, thus sensor fingerprinting not only identifies devices but also makes it possible to create non-erasable fingerprints.

In this work, we focus on distinguishing devices via mobile device sensors. To this end, a large dataset, larger than 25 GB, which consists of accelerometer and gyroscope sensor data from 21 distinct devices is utilized. We employ different classification methods on extracted 40 features based on various time windows from mobile sensors. Namely, we use random forest, gradient boosting machine, and generalized linear model classifiers. In conclusion, we obtain the highest accuracy as 97% from various experiments in identifying 21 devices using gradient boosting machine on the data from accelerometer and gyroscope sensors.