We investigate the possibility of detection of injection attacks using data analytics techniques

in this thesis. The automotive industry is innovating the modern vehicles towards connectivity by

interfacing them with various external entities. These entities are exposing the automobile to cyber

attacks instead of ensuring its safety. Therefore it is important to consider the security aspect while

developing these interfaces. Firstly, we try understand the automobile network architecture and the

possible security threats associated with it. Next, we examine the various possible cyber-attacks

on automobiles described in the literature. We experiment and analyze the attack scenarios by

performing injection attacks on a vehicle. We collect the data during the injection attacks and

apply multiple data analysis techniques. These techniques build a model based on data during

normal operation. The observations from the data collected during injection attacks is fit into

these techniques. The data points that do not fit the model are termed as attack points. Finally

we examine and analyze the results and their accuracy in detecting injection attacks.