The number of sensors, actuators and electronic control units present in cars have increased in the last few years. The Internet-of-Things (IoT) model has transformed modern vehicles into a co-engineered interacting network of physical and computational components. Vehicles have become a complex cyber-physical system where context detection has become a challenge. In this paper, we present a rule based approach for context detection in vehicles. We also discuss various attack surfaces and vulnerabilities in vehicular IoT. We propose a system which collects data from the CAN bus and uses it to generate SWRL rules. We then reason over these rules to mine vehicular context. We also showcase a few use-cases as examples where our system can detect if a vehicle is in an unsafe/anomalous state.
['Sandeep Nair Narayanan', 'Sudip Mittal', 'Anupam Joshi']
Using semantic technologies to mine vehicular context for security