Research Seminar on 29.03.2023 16:00
Room video conference

Evaluating Different QUIC Scan Approaches

Final talk for Bachelor's Thesis
Florian Gebauer (Zirngibl, Sattler)

Towards an Efficiently Queryable Data Structure for Large-Scale TLS Scans

Final talk for Bachelor's Thesis
Theresa Gräbner (Sattler, Zirngibl)

Structure and Performance of Smart Contracts in Different Ecosystems

Final talk for Master's Thesis
Simon Borowski (Rezabek, von Seck, Glas)

Research Seminar on 05.04.2023 16:00
Room video conference

Cache Efficient Hashing-Based Data Structures in P4

Final talk for Bachelor's Thesis
Carlos Nechwatal (Simon, Gallenmüller)

Towards Zero Knowledge – Adaptive Client-Side Mailbox Migration for End-to-End Encrypted Email Communication

Intermediate talk for Master's Thesis
Johannes Münichsdorfer (Kinkelin)

Accepted for Publication

A Multi-Tenancy System Architecture for Online Examinations

Authors: Jonas Andre, Johannes Naab, Benedikt Jaeger, Georg Carle, Leander Seidlitz, Stephan Günther

Nationale Konferenz IT-Sicherheitsforschung, Postersession

Robuste und sichere Kommunikation für die Mobilfunknetze der Zukunft

Authors: Kilian Holzinger, Henning Stubbe, Manuel Simon, Sebastian Gallenmüller, Georg Carle

Passive and Active Measurement

DissecTLS: A Scalable Active Scanner for TLS Server Configurations, Capabilities, and TLS Fingerprinting

Collecting metadata from Transport Layer Security (TLS) servers on a large scale allows to draw conclusions about their capabilities and configuration. This provides not only insights into the Internet but it enables use cases like detecting malicious Command and Control (C &C) servers. However, active scanners can only observe and interpret the behavior of TLS servers, the underlying configuration and implementation causing the behavior remains hidden. Existing approaches struggle between resource intensive scans that can reconstruct this data and light-weight fingerprinting approaches that aim to differentiate servers without making any assumptions about their inner working. With this work we propose DissecTLS, an active TLS scanner that is both light-weight enough to be used for Internet measurements and able to reconstruct the configuration and capabilities of the TLS stack. This was achieved by modeling the parameters of the TLS stack and derive an active scan that dynamically creates scanning probes based on the model and the previous responses from the server. We provide a comparison of five active TLS scanning and fingerprinting approaches in a local testbed and on toplist targets. We conducted a measurement study over nine weeks to fingerprint C &C servers and analyzed popular and deprecated TLS parameter usage. Similar to related work, the fingerprinting achieved a maximum precision of 99 % for a conservative detection threshold of 100 %; and at the same time, we improved the recall by a factor of 2.8.

Authors: Markus Sosnowski, Johannes Zirngibl, Patrick Sattler, Georg Carle

2nd International Workshop on Negative Results in Pervasive Computing (PerFail 2023)

TSN Experiments Using COTS Hardware and Open-Source Solutions: Lessons Learned

Authors: Filip Rezabek*, Marcin Bosk*, Georg Carle, Jörg Ott

15th International Conference on COMmunication Systems & NETworkS, COMSNETS 2023, Bangalore, India, January 3-8, 2023

Experimental Research Reproducibility and Experiment Workflow Management

Authors: Yuri Demchenko, Sebastian Gallenmüller, Serge Fdida, Panayiotis Andreou, Cedric Crettaz, Mathias Kirkeng

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)

BFT-Blocks: The Case for Analyzing Networking in Byzantine Fault Tolerant Consensus

Authors: Richard Von Seck, Filip Rezabek, Benedikt Jaeger, Sebastian Gallenmüller, Georg Carle

Proceedings of the 1st International Workshop on Graph Neural Networking

Modeling TCP Performance Using Graph Neural Networks

TCP throughput and RTT prediction are essential to model TCP behavior and optimize network configurations. Flows adapt their sending rate to network parameters like link capacity or buffer size and interact with parallel flows. Especially the elastic behavior of TCP congestion control can vary, even when only slight changes in the network occur. Thus, existing analytical models for TCP behavior reach their limits due to the number and complexity of different algorithms. Machine learning approaches, in contrast, are often fixed to specific network topologies.This paper presents a TCP bandwidth and RTT prediction approach that can handle different algorithms and topologies. For this, we utilize Gated Graph Neural Networks and simulated network traffic. We evaluate different encodings of the input data into graphs and how network size, number of flows, and TCP algorithms influence prediction accuracy. Additionally, we quantify the impact of different input features on our models. We show that Graph Neural Networks can be used to model TCP behavior. The resulting models can predict RTT with a median relative error of 2.29% and throughput with an error of 13.31%.

Authors: Benedikt Jaeger, Max Helm, Lars Schwegmann, Georg Carle


How Low Can You Go? A Limbo Dance for Low-Latency Network Functions

Authors: Sebastian Gallenmüller, Florian Wiedner, Johannes Naab, Georg Carle

e-Prüfungs Symposium

Quantifizierung des Lernerfolgs in Präsenz vs. Nutzung von Aufzeichnungen

Authors: Stephan Günther, Georg Carle, Adrian Pesch

PerFail'23: Best Paper Award

Best Paper Award at the PerFail 2023

Our publication "TSN Experiments Using COTS Hardware and Open-Source Solutions: Lessons Learned" has been awarded with the Best Paper Award at the Second International Workshop on Negative Results in Pervasive Computing (PerFail 2023), co-located with IEEE Pervasive Computing (PerCom) 2023, ...


TUM Research Groups Selected as Global Winners for Blockchain and Education Program offered by Algorand Foundation

The Algorand protocol [1] is a carbon-zero Layer 1 Blockchain technology, founded by the Turing Award winner and MIT professor Silvio Micali. Based on pure Proof-of-Stake (POS) consensus, Algorand currently supports 1000 ...

TMA'22: Best Paper Award

Best Paper Award at TMA 2022

Our publication "Active TLS Stack Fingerprinting: Characterizing TLS Server Deployments at Scale" has been awarded with the Best Paper Award at the Network Traffic Measurement and Analysis Conference (TMA 2022).

The publication is a collaboration with Claas Grohnfeldt, Michele ...

CCNC'20: Best Demo Award

Best Demo Award at CCNC 2020

Our demo of NCSbench has been awarded the Best Demo Award at the IEEE Consumer Communications and Networking Conference (CCNC'20) in Las Vegas, Nevada, USA.

The demo presented NCSbench a platform consisting of a networked control system (NCS) and ...

ANCS'19: Best Paper Award

Best Paper Award at ANCS 2019

Our publication The Case for Writing Network Drivers in High-Level Programming Languages has been awarded with the Best Paper Award at the ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS'19) in Cambridge, United Kingdom.

The publication ...