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Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection — arXiv:2610.02552

Source: arXiv
Original: https://arxiv.org/abs/2610.02552
License (as archived): CC0-1.0
Archived: 2026-10-05T22:50:07.029Z
SHA-256 of the archived text: b6ca3c8b05511d444971c151bbebb928b93885171911309cd44337f1db88d892
Stable URL of this page: https://metraipolis.com/documento/dc_ccdf338dbb89

How to cite

Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection — arXiv:2610.02552 — arXiv, https://arxiv.org/abs/2610.02552 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ccdf338dbb89 (Metraipolis archive dc_ccdf338dbb89, sha256 b6ca3c8b05511d444971c151bbebb928b93885171911309cd44337f1db88d892)

BibTeX

@misc{dcccdf338dbb89,
  title = {Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection — arXiv:2610.02552},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.02552},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ccdf338dbb89; sha256 b6ca3c8b05511d444971c151bbebb928b93885171911309cd44337f1db88d892},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.02552. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most adversarial research against them perturbs the feature vector or the traffic that produces it, and depends on gradient access, repeated model queries, or a learned model of benign traffic. A smaller line of work reshapes packet timing without querying the detector, but makes malicious traffic mimic a learned model of benign timing. Across these approaches, one assumption of industrial monitoring pipelines has received little attention: temporal synchronization. An IDS reconstructs operational state by aggregating telemetry into sliding or tumbling windows, so its view depends not only on what is observed but on when each observation falls relative to a window boundary. We introduce the Phantom State Attack (PSA), which exploits that dependence under a passive, zero-query threat model. Rather than modifying packets, perturbing features, querying the classifier, or fitting any model of benign traffic, PSA injects bounded timing drift calibrated to the attack flow's own inter-arrival variability, moving observations across the nearest window boundary by the minimal shift needed. The IDS then reconstructs a phantom state that diverges from the true process state. We evaluate PSA on ToN-IoT and CIC IIoT 2025 (DataSense), against Random Forest, MLP and XGBoost, measuring detection degradation, synchronization distortion, stealth, and attacker cost. PSA degrades detection on flows carrying enough packets for window-boundary redistribution, and leaves others almost unchanged, so its effect is conditional. A query-based baseline reaches higher raw success but needs many queries per window, while PSA needs none. The results identify temporal aggregation as an attack surface reachable under weaker assumptions than prior evasion techniques.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.02552 — 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 2026-10-05T22:45:55.772Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).