@inproceedings{pmksw-psant-26,
  author = {Jannik Presberger and Alexander M{\"a}nnel and Maynard Koch and Thomas C. Schmidt and Matthias W{\"a}hlisch and Bjoern Andres},
  title = {{Poster: Structural Analysis of Network Telescope Data Using Contrastive Learning and Correlation Clustering}},
  booktitle = {Proc. of ACM Internet Measurement Conference (IMC)},
  pages = {},
  year = {2026},
  publisher = {ACM},
  address = {New York},
  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.},
  doi = {10.1145/3777912.3847595},
  url = {https://doi.org/10.1145/3777912.3847595},
  slides = {},
  video = {},
  topic = {nsec|imeasurement},
  note = {accepted for publication},
}