In-vehicle detection of targeted CAN bus attacks

Most vehicles use the controller area network bus for communication between their components. Attackers who have already penetrated the in-vehicle network often utilize this bus in order to take control of safety-relevant components of the vehicle. Such targeted attack scenarios are often hard to detect by network intrusion detection systems because the specific payload is usually not contained within their training data sets. In this work, we describe an intrusion detection system that uses decision trees that have been modelled through genetic programming. We evaluate the advantages and disadvantages of this approach compared to artificial neural networks and rule-based approaches. For this, we model and simulate specific targeted attacks as well as several types of intrusions described in the literature. The results show that the genetic programming approach is well suited to identify intrusions with respect to complex relationships between sensor values which we consider important for the classification of specific targeted attacks. However, the system is less efficient for the classification of other types of attacks which are better identified by the alternative methods in our evaluation. Further research could thus consider hybrid approaches.