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CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts — arXiv:2608.13905

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13905
License (as archived): CC0-1.0
Archived: 2026-10-19T02:21:11.853Z
SHA-256 of the archived text: 1075e634aabc49cc0c538e1f73f694b9a953f95d0dd261280d400d2b3f4bc41f
Stable URL of this page: https://metraipolis.com/documento/dc_fd77ae293d5e

How to cite

CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts — arXiv:2608.13905 — arXiv (en), https://arxiv.org/abs/2608.13905 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fd77ae293d5e (Metraipolis archive dc_fd77ae293d5e, sha256 1075e634aabc49cc0c538e1f73f694b9a953f95d0dd261280d400d2b3f4bc41f)

BibTeX

@misc{dcfd77ae293d5e,
  title = {CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts — arXiv:2608.13905},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13905},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fd77ae293d5e; sha256 1075e634aabc49cc0c538e1f73f694b9a953f95d0dd261280d400d2b3f4bc41f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13905. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

HTTPS website fingerprinting (WF) aims to identify visited websites from metadata observable in encrypted traffic. However, real-world deployments introduce a significant out-of-distribution (OOD) problem caused by temporal and geographic changes, while previously unseen websites are common in open-world scenarios. Existing methods primarily learn from raw TCP packet sequences and struggle to capture stable and generalizable website representations, resulting in performance degradation under practical conditions. We propose CipherSight, a TLS-record-based hierarchical framework for robust HTTPS WF. Unlike existing approaches that rely on TCP packet sequences and are sensitive to transport-layer artifacts, CipherSight learns website representations from TLS records by jointly encoding multiple record-level attributes. It introduces a hierarchical architecture that captures both intra-flow dependencies among TLS records and inter-flow interactions across concurrent flows, enabling the model to exploit structural patterns in HTTPS traffic. Besides, to learn robust representations, CipherSight employs a masked record modeling (MRM) task to capture contextual traffic semantics and leverages fine-grained record-resource annotations as privileged supervision through structure-aware objectives and semantic distillation. Experiments show that CipherSight achieves 95.41% accuracy across more than 2,000 website classes in the closed-world setting and maintains over 90% accuracy under both temporal and geographic drift, consistently outperforming all evaluated baselines.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-17 in cs.AI).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2608.13905 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T02:02:02.882Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).