CONNECT

V2X communication enhances road safety but is vulnerable to data manipulation attacks that could lead to safety-critical incidents, motivating the use of Misbehavior Detection Systems (MDSs). The evaluation of MDSs typically relies on simulated V2X scenarios and attacks. To enable reproducible evaluations, publicly available datasets containing V2X messages are important. Existing datasets have several limitations, including limited attack diversity and missing training/validation/test sets for machine-learning-based MDSs. Therefore, we introduce VeReMi NextGen, generated using the InTAS traffic scenario and Eclipse MOSAIC. The dataset includes urban and highway scenarios, three driver profiles, 15 attack types, and training/validation/test sets, thereby providing significantly broader coverage than previous datasets. The attacks were designed to be more advanced and harder to detect than those in the predecessor VeReMi Extension, as confirmed by an evaluation using a state-of-the-art MDS. Our contribution includes the dataset and a publicly available dataset generator, enabling easy integration of additional attacks and entities, such as vulnerable road users.

Ensuring the semantic correctness of exchanged kinematic data is critical for safety-critical Vehicle-to-Everything (V2X) applications. While onboard Misbehaviour Detection (MBD) mechanisms help address this issue, their effectiveness is inherently limited by the vehicle’s local view. This work investigates whether lightweight, rule-based MBD can be significantly enhanced through infrastructure support without increasing computational complexity. We adopt a Trust Assessment Framework to control the inclusion of V2X data into the vehicle’s Extended Perception Map (EPM), based on trust levels derived from MBD outputs. We compare a standalone setup relying on local evidence only with a federated setup in which the infrastructure aggregates Misbehaviour Reports from multiple vehicles, assesses node trustworthiness, and disseminates this information back to vehicles. Simulation results under kinematic falsification attacks show that the federated setup consistently outperforms the standalone one in filtering altered observations.

Subjective Logic (SL) trust discounting enables trust transitivity along referral paths and is widely applied in distributed cyber-physical systems and ad-hoc networks. When used for adversarial integrity-focused trust assessment, however, established discounting operators exhibit two systematic effects: (i) short chains with conflicting or jointly negative opinions may produce discounted results dominated by uncertainty rather than disbelief; and (ii) under repeated sequential composition, several operators attenuate committed mass multiplicatively, driving uncertainty toward one as path length increases. These behaviors conflict with weakest-link integrity semantics in which a single compromised node should dominate the trust assessment. This paper introduces disbelief-favouring trust discounting (DF), an SL operator that propagates maximum disbelief along a path and redistributes remaining belief–uncertainty mass proportionally. We analyze the structural mechanism underlying uncertainty accumulation in established operators, formalize design requirements for adversarial multi-hop integrity assessment, and evaluate DF against existing operators using controlled synthetic chain experiments and Monte Carlo simulations with probabilistic compromise and detection. Results show that DF avoids uncertainty convergence and improves block-averaged F1 across required trust levels over increasing chain lengths.

Vehicular communication via V2X networks significantly improves road safety, but is vulnerable to data manipulation, which can lead to serious incidents. To address this threat, misbehavior detection systems (MBDs) have been developed to detect such misbehavior. In order to enhance the detection of data manipulation, trust assessment in V2X networks has recently gained increasing attention. Trust assessment takes into account the output of various security mechanisms such as MBDs or Intrusion Detection Systems (IDSs) to detect misbehavior. One particular challenge in trust assessment is the appropriate quantification of the output of these security mechanisms into trust opinions. In this paper, we propose a trust quantification methodology that transforms the output of an MBD into a subjective logic opinion. Furthermore, we apply a hyperparameter optimization approach to determine the optimal parameter set for an MBD. Our evaluation using three MBD variants shows that the optimization approach significantly increased the detection-performance of all MBDs. The MBD variant that used the optimization approach and our proposed trust quantification methodology achieved the best performance, increasing the F1 score by over 13% compared to other state-of-the-art MBD variants analyzed in this work.

Future vehicles and infrastructure will rely on data from external entities such as other vehicles via V2X communication for safety-critical applications. Malicious manipulation of this data can lead to safety incidents. Earlier works proposed a trust assessment framework (TAF) to allow a vehicle or infrastructure node to assess whether it can trust the data it received. Using subjective logic, a TAF can calculate trust opinions for the trustworthiness of the data based on different types of evidence obtained from diverse trust sources. One particular challenge in trust assessment is the appropriate quantification of this evidence. In this paper, we introduce different quantification methods that transform evidence into appropriate subjective logic opinions. We suggest quantification methods for different types of evidence: security reports, misbehavior detection reports, intrusion detection system alerts, GNSS spoofing scores, and system integrity reports. Our evaluations in a smart traffic light system scenario show that the TAF detects attacks with an accuracy greater than 96% and intersection throughput increased by 42% while maintaining safety and security, when using our proposed quantification methods.

Vehicular communication via V2X networks increases road safety, but is vulnerable to data manipulation which can lead to serious incidents. Existing security systems, such as misbehavior detection systems, have limitations in detecting and mitigating such threats. To address these challenges, we have implemented a software prototype of a Trust Assessment Framework (TAF) that assesses the trustworthiness of received V2X data by integrating evidence from multiple trust sources. This interactive demonstration illustrates the quantification of trust for a smart traffic light system application. We demonstrate the impact of varying evidence coming from a misbehavior detection system and a security report generator on the trust assessment process. We also showcase internal processing steps within our TAF when receiving new evidence, up to and including the eventual decision making on the trustworthiness of the received V2X data.

Connected and automated vehicles rely on data from various entities to support safety-critical applications such as Cooperative Adaptive Cruise Control (CACC). However, unauthorized data manipulation through, for example, data injection attacks can compromise vehicle safety and lead to incidents. Existing vehicular security mechanisms, such as Misbehavior Detection System (MBD), have limitations in detecting and mitigating all types of threats on their own. To address these limitations, our prior work has proposed the concept of a Trust Assessment Framework (TAF), which assesses data trustworthiness by combining evidence from multiple security systems operating as trust sources. However, TAF as a concept has not been extensively evaluated in safety-critical Cooperative Driving (CD) applications. In this work, we refine the architecture of the TAF and implement a software prototype based on it. We integrate the TAF prototype with a CACC simulation environment and implement three types of data injection attacks. We demonstrate that by incorporating multiple security mechanisms as trust sources, the TAF significantly improves attack detection performance and reduces the number of crashes by 86% compared to using a single security mechanism, such as MBD.