  Despite advancements in vehicle security systems, over the last decade,
auto-theft rates have increased, and cyber-security attacks on
internet-connected and autonomous vehicles are becoming a new threat. In this
paper, a deep learning model is proposed, which can identify drivers from their
driving behaviors based on vehicle telematics data. The proposed
Long-Short-Term-Memory (LSTM) model predicts the identity of the driver based
on the individual's unique driving patterns learned from the vehicle telematics
data. Given the telematics is time-series data, the problem is formulated as a
time series prediction task to exploit the embedded sequential information. The
performance of the proposed approach is evaluated on three naturalistic driving
datasets, which gives high accuracy prediction results. The robustness of the
model on noisy and anomalous data that is usually caused by sensor defects or
environmental factors is also investigated. Results show that the proposed
model prediction accuracy remains satisfactory and outperforms the other
approaches despite the extent of anomalies and noise-induced in the data.
