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

Köstler, J., Reiser, H.P., Hauck, F.J. and Habiger, G. 2023. Fluidity: location-awareness in replicated state machines. 38th ACM/SIGAPP Symp. on Appl. Comp. – SAC (Mar. 2023). [accepted for publication]
In planetary-scale replication systems, the overall response delay is greatly influenced by the geographical distances between client and server nodes. Current systems define the replica locations statically during startup time. However, the selected locations might be suboptimal for the clients, and the client request origin distribution may change over time, so a different replica placement may provide lower overall request latencies. In this work, we propose a locationaware replicated state machine that is able to adapt the geographic location of its replicas dynamically during runtime to locations geographically closer to client request origins. Our prototype is able to observe emerging optimization potentials and to reduce the overall request latency for the majority of clients by adapting its replica locations to the time-dependent optimum placement during real-world use case evaluations, whereby the absolute performance gain is dependent on the respective usage scenario.
Schillings, C., Meißner, D., Erb, B., Schultchen, D., Bendig, E. and Pollatos, O. 2023. A chatbot-based intervention with ELME to improve stress and health-related parameters in a stressed sample: Study protocol of a randomised controlled trial. Frontiers in Digital Health. 5, (Jan. 2023), 17. [accepted for publication]
Background: Stress levels in the general population had already been increasing in recent years, and have subsequently been exacerbated by the global pandemic. One approach for innovative online-based interventions are “chatbots” – computer programs that can simulate a text-based interaction with human users via a conversational interface. Research on the efficacy of chatbot-based interventions in the context of mental health is sparse. The present study is designed to investigate the effects of a three-week chatbot-based intervention with the chatbot ELME, aiming to reduce stress and to improve various health-related parameters in a stressed sample. Methods: In this multicenter, two-armed randomised controlled trial with a parallel design, a three-week chatbot-based intervention group including two daily interactive intervention sessions via smartphone (á 10-20 min.) is compared to a treatment-as-usual control group. A total of 130 adult participants with a medium to high stress levels will be recruited in Germany. Assessments will take place pre-intervention, post-intervention (after three weeks), and follow-up (after six weeks). The primary outcome is perceived stress. Secondary outcomes include self-reported interoceptive accuracy, mindfulness, anxiety, depression, personality, emotion regulation, psychological well-being, stress mindset, intervention credibility and expectancies, affinity for technology, and attitudes towards artificial intelligence. During the intervention, participants undergo ecological momentary assessments. Furthermore, satisfaction with the intervention, the usability of the chatbot, potential negative effects of the intervention, adherence, potential dropout reasons, and open feedback questions regarding the chatbot are assessed post-intervention. Discussion: To the best of our knowledge, this is the first chatbot-based intervention addressing interoception, as well as in the context with the target variables stress and mindfulness. The design of the present study and the usability of the chatbot were successfully tested in a previous feasibility study. To counteract a low adherence of the chatbot-based intervention, a high guidance by the chatbot, short sessions, individual and flexible time points of the intervention units and the ecological momentary assessments, reminder messages, and the opportunity to postpone single units were implemented.
Kargl, F., Erb, B. and Bösch, C. 2023. Defining Privacy. Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics. C. Montag and H. Baumeister, eds. Springer International Publishing. 461–463.
Kleber, S. and Kargl, F. 2022. Refining Network Message Segmentation with Principal Component Analysis. Proceedings of the tenth annual IEEE Conference on Communications and Network Security (Austin, TX, USA, Oct. 2022).
Reverse engineering of undocumented protocols is a common task in security analyses of networked services. The communication itself, captured in traffic traces, contains much of the necessary information to perform such a protocol reverse engineering. The comprehension of the format of unknown messages is of particular interest for binary protocols that are not human-readable. One major challenge is to discover probable fields in a message as the basis for further analyses. Given a set of messages, split into segments of bytes by an existing segmenter, we propose a method to refine the approximation of the field inference. We use principle component analysis (PCA) to discover linearly correlated variance between sets of message segments. We relocate the boundaries of the initial coarse segmentation to more accurately match with the true fields. We perform different evaluations of our method to show its benefit for the message format inference and subsequent analysis tasks from literature that depend on the message format. We can achieve a median improvement of the message format accuracy across different real-world protocols by up to 100 %.
Kleber, S., Stute, M., Hollick, M. and Kargl, F. 2022. Network Message Field Type Classification and Recognition for Unknown Binary Protocols. Proceedings of the DSN Workshop on Data-Centric Dependability and Security (Baltimore, Maryland, USA, Jun. 2022).
Reverse engineering of unknown network protocols based on recorded traffic traces enables security analyses and debugging of undocumented network services. In particular for binary protocols, existing approaches (1) lack comprehensive methods to classify or determine the data type of a discovered segment in a message, e.,g., a number, timestamp, or network address, that would allow for a semantic interpretation and (2) have strong assumptions that prevent analysis of lower-layer protocols often found in IoT or mobile systems. In this paper, we propose the first generic method for analyzing unknown messages from binary protocols to reveal the data types in message fields. To this end, we split messages into segments of bytes and use their vector interpretation to calculate similarities. These can be used to create clusters of segments with the same type and, moreover, to recognize specific data types based on the clusters' characteristics. Our extensive evaluation shows that our method provides precise classification in most cases and a data-type-recognition precision of up to 100% at reasonable recall, improving the state-of-the-art by a factor between 1.3 and 3.7 in realistic scenarios. We open-source our implementation to facilitate follow-up works.

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Marion Köhler
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Institut für Verteilte Systeme
Universität Ulm
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