DeepFP: A Deep Learning Framework For User Fingerprinting via Mobile Motion Sensors

In this paper, we propose a deep learning framework for user fingerprinting via mobile motion sensors, DeepFP, which can identify and track users based on their behavioral patterns while interacting with the smartphone. Existing machine learning techniques for user identification are classification-oriented and thus are not amenable easily to large-scale, real world deployment. They need to be trained on all the users whom they want to identify. DeepFP exploits metric learning techniques and deep neural networks to address the challenges of current user identification techniques. We leverage feature embedding to directly extract informative features and map input samples to a discriminative lower-dimensional space, where recurrent neural networks are used to model the temporal information of data. DeepFP does not need to re-train to identify new users which makes it feasible to be used in real world scenarios with a huge number of users, without needing a large number of training samples. Experiments on a publicly available mobile sensors dataset and comparison with other embedding methods depict the effectiveness of DeepFP.