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.

State-machine replication is an established concept to build fault-tolerant services, whereby a consensus protocol is used to enforce a deterministic request order throughout a set of redundant replicas. This ensures that state-altering requests are executed in the same order throughout all replicas. There is an established read-only optimization, which allows read requests to bypass the consensus protocol to reduce the end-to-end latency, but requires the clients to wait for more responses for all requests, including ordered requests, to guarantee linearizability. In this paper, we propose a novel approach that introduces speculatively-ordered read (SOR) requests and allows to reduce the required response quorum for ordered requests, while still preserving linearizability. We conducted a series of experiments with wide-area and cluster deployments, which show that our approach can significantly reduce the number of read-write conflicts and thereby drastically improve the request processing latency of ordered requests.