Biblio

AbstractCyber assurance of heavy trucks is a major concern with new designs as well as with supporting legacy systems. Many cyber security experts and analysts are used to working with traditional information technology (IT) networks and are familiar with a set of technologies that may not be directly useful in the commercial vehicle sector. To help connect security researchers to heavy trucks, a remotely accessible testbed has been prototyped for experimentation with security methodologies and techniques to evaluate and improve on existing technologies, as well as developing domain-specific technologies. The testbed relies on embedded Linux-based node controllers that can simulate the sensor inputs to various heavy vehicle electronic control units (ECUs). The node controller also monitors and affects the flow of network information between the ECUs and the vehicle communications backbone. For example, a node controller acts as a clone that generates analog wheel speed sensor data while at the same time monitors or controls the network traffic on the J1939 and J1708 networks. The architecture and functions of the node controllers are detailed. Sample interaction with the testbed is illustrated, along with a discussion of the challenges of running remote experiments. Incorporating high fidelity hardware in the testbed enables security researchers to advance the state of the art in hardening heavy vehicle ECUs against cyber-attacks. How the testbed can be used for security research is presented along with an example of its use in evaluating seed/key exchange strength and in intrusion detection systems (IDSs).

The landscape of automotive in-vehicle networks is changing driven by the vast options for infotainment features and progress toward fully-autonomous vehicles. However, the security of automotive networks is lagging behind feature-driven technologies, and new vulnerabilities are constantly being discovered. In this paper, we introduce a road map towards a security solution for in-vehicle networks that can detect anomalous and failed states of the network and adaptively respond in real-time to maintain a fail-operational system.