Abstract

Jannik Presberger, Alexander Männel, Maynard Koch, Thomas C. Schmidt, Matthias Wählisch, Bjoern Andres,
Poster: Structural Analysis of Network Telescope Data Using Contrastive Learning and Correlation Clustering,
In: Proc. of ACM Internet Measurement Conference (IMC), p. 1217–1218, ACM : New York, 2026.
[html][BibTeX][Abstract]

Abstract: In this poster, we start exploring whether ML-based methods can extract meaningful structural properties from a set of Internet packets originating from different sources. Unlike prior work, we aim for a technique that requires neither pretraining, labeling nor supervision, and can be executed locally, enabling sovereign and reproducible research. As a first approach, we combine contrastive learning and correlation clustering to identify groups of Internet scanners. Our analysis is based on data from the UCSD network telescope.

 

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