Connected, Cooperative, and Autonomous Mobility (CCAM) will take intelligent transportation to a new level of complexity. CCAM systems can be thought of as complex Systems-of-Systems (SoSs). They pose new challenges to security as consequences of vulnerabilities or attacks become much harder to assess. In this paper, we propose the use of a specific type of a trust model, called subjective trust network, to model and assess trustworthiness of data and nodes in an automotive SoS. Given the complexity of the topic, we illustrate the application of subjective trust networks on a specific example, namely Cooperative Intersection Management (CIM). To this end, we introduce the CIM use-case and show how it can be modelled as a subjective trust network. We then analyze how such trust models can be useful both for design time and run-time analysis, and how they would allow us a more precise quantitative assessment of trust in automotive SoSs. Finally, we also discuss the open research problems and practical challenges that need to be addressed before such trust models can be applied in practice.

Smart traffic lights systems (STLSs) are a promising approach to improve traffic efficiency at intersections. They rely on the information sent by vehicles via C2X communication (like in cooperative awareness messages (CAMs)) at the managed intersection. While there exists a large body of work on privacy-enhancing technologies (PETs) for cooperative Intelligent Transport Systems (cITS) in general, such PETs like changing pseudonyms often impact the performance of cITS applications. This paper analyzes the extent to which different PETs affect the performance of two types of STLSs, a phase-based and a reservation-based STLS. These are implemented in SUMO and combined with four different PETs. Through extensive simulations we then investigate the impact of those PETs on STLS performance metrics like time loss, waiting time, fuel consumption, and average velocity. Our analysis shows that the impact of PETs on performance varies greatly depending on the type of STLS. Finally, we propose a hybrid STLS which is a combination of the two STLS types as a potential solution for limiting the negative impact of PETs on performance.

Today’s vehicles run various safety-critical applications requiring data input from diverse in-vehicle components. Adaptive Cruise Control (ACC), for example, can rely on the data input from components such as lidar, radar, GNSS, and cameras. Malicious manipulation of any of this data compromises the data integrity and can result in safety incidents or accidents on the road. Security mechanisms like intrusion detection can be in place; however, they can not reliably assess the consequences of attacks on a system level or for arbitrary subsystems. In this paper, we present a Trust Assessment Framework (TAF) that allows an in-vehicle application in a complex System-of-Systems to assess whether it can trust the integrity of its input data.The TAF assesses the trustworthiness of every component in the data flow chain based on collected evidence. We explain this concept with the example of ACC and show case two ossible implementations of the TAF inside a vehicle.

Vulnerable or malicious third-party components introduce vulnerabilities into the software supply chain. Software Composition Analysis (SCA) is a method to identify direct and transitive dependencies in software projects and assess their security risks and vulnerabilities. In this paper, we investigate two open source SCA tools, Eclipse Steady (ES) and OWASP Dependency Check (ODC), with respect to vulnerability detection in Java projects. Both tools use different vulnerability detection methods. ES implements a code-centric and ODC a metadata-based approach. Our study reveals that both tools suffer from false positives. Furthermore, we discover that the success of the vulnerability detection depends on the underlying vulnerability database. Especially ES suffered from false negatives because of the insufficient vulnerability information in the database. While code-centric and metadata-based approaches offer significant potential, they also come with their respective downsides. We propose a hybrid approach assuming that combining both detection methods will lead to less false negatives and false positives.