Detection of injection attacks on in-vehicle network using data analytics

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

Detection of injection attacks on in-vehicle network using data analytics

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.</p