Cyberattacks on Adaptive Cruise Controls and Emergency Braking Systems: Adversary Models, Impact Assessment, and Countermeasures

Abstract

In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.