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Tracking Trackers: ML-based Personal Tracker Detection in Crowded IoT Environments

Katharina O. E. Müller, Stefan Saxer, Daria Schumm, Bruno Rodrigues, Burkhard Stiller

IEEE Symposium on Computers and Communications (ISCC) (venue) · Vilamoura, Algarve, Portugal, 23-26 June 2026 · 2026

Preprint
Figure from Tracking Trackers: ML-based Personal Tracker Detection in Crowded IoT Environments

Personal BLE trackers are handy for finding your keys and dangerous when used to follow a person through a crowded station. Earlier binary detectors (tracker vs not) break down in dense, heterogeneous environments. This paper introduces a categorical classifier that identifies popular BLE trackers and their operational states from a 200-hour dataset, reaching over 99% accuracy and validated live in a busy central station. A vendor-agnostic step toward robust, real-world anti-stalking detection.

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Cite this paper (BibTeX)
@inproceedings{muller2026-tracking-trackers,
  author    = {Katharina O. E. Müller and Stefan Saxer and Daria Schumm and Bruno Rodrigues and Burkhard Stiller},
  title     = {{Tracking Trackers: ML-based Personal Tracker Detection in Crowded IoT Environments}},
  booktitle = {IEEE Symposium on Computers and Communications (ISCC)},
  year      = {2026},
  address   = {Vilamoura, Algarve, Portugal},
  url       = {https://sensing-group.com/files/papers/2026-iscc-tracking-trackers.pdf}
}