  As automobiles become intelligent, automobile theft methods are evolving
intelligently. Therefore automobile theft detection has become a major research
challenge. Data-mining, biometrics, and additional authentication methods have
been proposed to address automobile theft, in previous studies. Among these
methods, data-mining can be used to analyze driving characteristics and
identify a driver comprehensively. However, it requires a labeled driving
dataset to achieve high accuracy. It is impractical to use the actual
automobile theft detection system because real theft driving data cannot be
collected in advance. Hence, we propose a method to detect an automobile theft
attempt using only owner driving data. We cluster the key features of the owner
driving data using the k-means algorithm. After reconstructing the driving data
into one of these clusters, theft is detected using an error from the original
driving data. To validate the proposed models, we tested our actual driving
data and obtained 99% accuracy from the best model. This result demonstrates
that our proposed method can detect vehicle theft by using only the car owner's
driving data.
