Forschung

Unser Institut beschäftigt sich mit einem breiten Themenspektrum wie Skalierbarkeit, Zuverlässigkeit, Sicherheit und Datenschutz, Selbstorganisation und Beherrschbarkeit von Komplexität in Verteilten Systemen in einer Vielzahl von Einsatzszenarien wie Cloud-Computing oder Fahrzeug-Fahrzeug-Kommunikation.

Lehre

In der Lehre decken wir das gesamte Spektrum von Rechnernetzen, über verteilte Systeme bis hin zu Sicherheit und Privacy-Schutz ab. Unsere noch offenen Abschlussarbeiten und Projektarbeiten finden Sie auf den entsprechenden Webseiten. Für Prüfungen beachten Sie bitte unsere Hinweise.

Soziale Medien

Unsere letzten Publikationen

Hermann, A., Remmers, J.N., Eisermann, D., Erb, B. and Kargl, F. 2026. VeReMi NextGen: A Dataset for Evaluating Misbehavior Detection Systems in VANETs. 2026 IEEE Vehicular Networking Conference (VNC) (Montreal, Canada, Jun. 2026). [accepted for publication]
Bassi, F., Zhang , J., Jemaa, I.B., Kargl, F. and Erb, B. 2026. Improving Misbehaviour Detection Through Infrastructure Support Without Raising Complexity. 2026 IEEE 103rd Vehicular Technology Conference (VTC2026-Spring) (Jun. 2026). [accepted for publication]
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.
Hermann, A., Füllhase, J. and Kargl, F. 2026. Enabling Vulnerability Awareness in V2X Networks Using Encrypted SBOMs. 2026 IEEE Vehicular Networking Conference (VNC) (Montreal, Canada, Jun. 2026). [accepted for publication]
Trkulja, N., Erb, B. and Kargl, F. 2026. Disbelief-Favouring Trust Discounting for Adversarial Multi-Hop Trust Assessment using Subjective Logic. 2026 29th International Conference on Information Fusion (FUSION) (Trondheim, Norway, Jun. 2026). [accepted for publication]
Heß, A. and Hauck, F.J. 2026. Leveraging Speculative Ordering for Fast and Resilient Reads in BFT State-Machine Replication. 2026 21st European Dependable Computing Conference (EDCC) (Canterbury, UK, 2026). (acceptance rate: 40%)
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.

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Kontakt

Sekretariat

Marion Köhler
Jessica Reib
Email-Adresse Sekretariat
Telefon: +49 731 50-24140
Telefax: +49 731 50-24142

Postanschrift

Institut für Verteilte Systeme
Universität Ulm
Albert-Einstein-Allee 11
89081 Ulm

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James-Franck-Ring
Gebäude O27, Raum 349
89081 Ulm

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