WIP: A Trust Assessment Method for In-Vehicular Networks using Vehicle Risk Assessment - NDSS Symposium

Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

Future vehicles will run safety-critical applications that rely on data from entities within and outside the vehicle. Malicious manipulation of this data can lead to safety incidents. In our work, we propose a Trust Assessment Framework (TAF) that allows a component in a vehicle to assess whether it can trust the provided data. Based on a logic framework called Subjective Logic, the TAF determines a trust opinion for all components involved in processing or forwarding a data item. One particular challenge in this approach is the appropriate quantification of trust. To this end, we propose to derive trust opinions for electronic control units (ECUs) in an in-vehicle network based on the security controls implemented in the ECU, such as secure boot. We apply a Threat Analysis and Risk Assessment (TARA) to assess security controls at design time and use run time information to calculate associated trust opinions. The feasibility of the proposed concept is showcased using an in-vehicle application with two different scenarios. Based on the initial results presented in this paper, we see an indication that a trust assessment based on quantifying security controls represents a reasonable approach to provide trust opinions for a TAF.

View More Papers

Understanding the Internet-Wide Vulnerability Landscape for ROS-based Robotic Vehicles...

Wentao Chen, Sam Der, Yunpeng Luo, Fayzah Alshammari, Qi Alfred Chen (University of California, Irvine)

Read More

The Fault in Our Stars: An Analysis of GitHub...

Simon Koch, David Klein, and Martin Johns (TU Braunschweig)

Read More

Facilitating Threat Modeling by Leveraging Large Language Models

Isra Elsharef, Zhen Zeng (University of Wisconsin-Milwaukee), Zhongshu Gu (IBM Research)

Read More

Make your IoT environments robust against adversarial machine learning...

Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

Read More