This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.

The spread of connected vehicles is expected to multiply the effects of the growing penetration of cyberspace in our life, and with this it remarkably influences the vulnerability of society to cyberattacks in an unfavorable way. Accordingly, ZalaZONE - the Hungarian test track - has set up a working group to support the necessary methodological background for cybersecurity-related validation processes for the automotive industry. Therefore the paper aims to reconsider safety integrity levels in the automotive industry related to the field of cybersecurity. Following this, the article provides a comprehensive structure of integrity levels that serves the safety requirements of nowadays new cybersecurity challenges. To adapt the new cybersecurity integrity level architecture to the conventional automotive safety integrity level framework, the authors have verified a specific clustering model. To validate the aggregated probability of the possible cyberattack alternatives, the generated new cybersecurity rating scale is compared with the ASIL probability values by applying the Kolmogorov-Smirnov test. In the final step of the analysis, the possible practical application of the new framework is presented based on the identification of the derived probability value in the case of the investigated failure mode.

Road traffic signals and traffic infrastructure have a significant impact on the behaviour of highly automated or autonomous vehicles. However, the increase in automation does not always mean an advantage. Generally, highly automated vehicles strictly follow the traffic rules resulting in near-accident situations, although their goal is to avoid and reduce them. Malfunctions of the automated functions might cause surprising interventions while the vehicle is in motion, drivers cannot react in time and well. This paper highlights the potential danger and uncertainty of highly automated or autonomous vehicles in the context of the current conventional traffic infrastructure system. In the future, special consideration shall be given to the vehicle industry and traffic regulation makers on how the infrastructure should be adapted to automated vehicle functions to have a seamless shift towards automated driving. The paper sums up many problematic situations with two critical problems with sensitivity analysis. The first situation: the speed assist system (based on speed limit sign recognition) conflicts with the traffic infrastructure. The second situation is shown: the ACC (Adaptive Cruise Control) and LKA (Lane Assist) contradicts with the traffic infrastructure. These critical situations were investigated by using an high-fidelity automotive simulation software as proof of concept and were examined by accident reconstruction analysis software.