Kullanıcı alışkanlıklarına dayalı sensör tabanlı parmak izi sistemi ile akıllı cihaz tanıma

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 thesis, we focus on distinguishing devices via mobile device sensors. To this end, a large dataset, larger than 140 GB, which consists of accelerometer, gyroscope, pressure, light and gravity sensor data from 25 distinct devices is utilized. We employ different classification methods on extracted features based on various time windows from mobile sensors. Namely, we use random forest, gradient boosting machine, generalized linear model and artificial neural network. In conclusion, we obtain the highest accuracy as 96% from various experiments in identifying 25 devices using random forest on the data from accelerometer and gyroscope sensors.