  Driver identification is a momentous field of modern decorated vehicles in
the controller area network (CAN-BUS) perspective. Many conventional systems
are used to identify the driver. One step ahead, most of the researchers use
sensor data of CAN-BUS but there are some difficulties because of the variation
of the protocol of different models of vehicle. Our aim is to identify the
driver through supervised learning algorithms based on driving behavior
analysis. To determine the driver, a driver verification technique is proposed
that evaluate driving pattern using the measurement of CAN sensor data. In this
paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS
sensor and the sensors are listed under SAE J1979 statement. According to the
service of OBD-II, drive identification is possible. However, we have gained
two types of accuracy on a complete data set with 10 drivers and a partial data
set with two drivers. The accuracy is good with less number of drivers compared
to the higher number of drivers. We have achieved statistically significant
results in terms of accuracy in contrast to the baseline algorithm
