3 Wei-Han Lee Xiaochen Liu Yilin Shen Hongxia Jin Ruby Lee 2017 Secure Pick Up: Implicit Authentication When You Start Using the Smartphone ACM Symposium on Access Control Models and Technologies (SACMAT) Indianapolis 06/21/2017 Authentication, Security, Privacy, Machine Learning, Smartphone, Dynamic Time Warping, Mobile System We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background, our system implicitly observes usersâ phone pick-up movements, the way they bend their arms when they pick up a smartphone to interact with the device, to authenticate the users. Our SPU outperforms the state-of-the-art implicit authentication mechanisms in three main aspects: 1) SPU automatically learns the userâs behavioral pattern without requiring a large amount of training data (especially those of other users) as previous methods did, making it more deployable. Towards this end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW) algorithm to effectively quantify similarities between usersâ pick-up movements; 2) SPU does not rely on a remote server for providing further computational power, making SPU efficient and usable even without network access; and 3) our system can adaptively update a userâs authentication model to accommodate userâs behavioral drift over time with negligible overhead. Through extensive experiments on real world datasets, we demonstrate that SPU can achieve authentication accuracy up to 96.3% with a very low latency of 2.4 milliseconds. It reduces the number of times a user has to do explicit authentication by 32.9%, while effectively defending against various attacks.