Anti-theft problem has been challenging since it mainly depends on the existence of external devices to defend from thefts. Recently, driver behavior analysis using supervised learning has been investigated with the goal to detect burglary by identifying drivers. In this paper, we propose a data-driven technique, LiveDI, which uses driving behavior removing the use of external devices in order to identify drivers. The built model utilizes Gated Recurrent Unit (GRU) and Fully Convolutional Networks (FCN) to learn long-short term patterns of the driving behaviors from drivers. Additionally, we improve the training time by utilizing the Segmented Feature Generation (SFG) algorithm to reduce the state space where the driving behaviors are split with a time window for analysis. Extensive experiments are conducted which show the impact of parameters on our technique and verify that our proposed approach outperforms the state-of-the-art baseline methods.