Pith Number

pith:H7UOFT2P

pith:2026:H7UOFT2PB4PR5BP5PAIOSDJZVE

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DSTAN-Med: Dual-Channel Spatiotemporal Attention with Physiological Plausibility Filtering for False Data Injection Attack Detection in IoT-Based Medical Devices

Md Mehedi Hasan, Md Zakir Hossain, Rafiqul Islam

A dual-channel attention model plus a zero-parameter plausibility filter detects falsified vital signs on IoMT sensors with 7.4-8.3 point sensitivity gains over Transformer baselines.

arxiv:2605.14165 v1 · 2026-05-13 · cs.CR

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\pithnumber{H7UOFT2PB4PR5BP5PAIOSDJZVE}

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

DSTAN-Med achieves mean sensitivity gains of 7.4-8.3 percentage points over the strongest Transformer baseline (TranAD), with improvements significant at p < 0.01 (McNemar's test, Holm-Bonferroni correction).

C2weakest assumption

That the zero-parameter Physiological Plausibility Filter will not suppress genuine but unusual physiological states that still fall inside the chosen bounds, and that the synthetic or injected attack patterns used in the three evaluation corpora match the distribution of real-world FDI attacks.

C3one line summary

DSTAN-Med uses separate sensor-wise and time-wise attention plus a zero-parameter physiological filter to detect falsified vital signs, reporting 7.4-8.3 percentage point sensitivity gains over Transformer baselines on three public datasets.

References

55 extracted · 55 resolved · 1 Pith anchors

[1] A deep reinforcement learning-based robust intrusion detection system for securing IoMT healthcare networks, 2025

[2] Implementing anomaly-based intrusion detection for resource-constrained devices in IoMT networks, 2025

[3] Internet of medical things (IoMT) market size, share & industry analysis, 2025

[4] A novel internet of medical things hybrid model for cybersecurity anomaly detection, 2025

[5] Unpatched and outdated medical devices provide cyber attack opportunities, 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:11.421929Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3fe8e2cf4f0f1f1e85fd7810e90d39a904359874525ea4ace9a4f98a1532972a

Aliases

arxiv: 2605.14165 · arxiv_version: 2605.14165v1 · doi: 10.48550/arxiv.2605.14165 · pith_short_12: H7UOFT2PB4PR · pith_short_16: H7UOFT2PB4PR5BP5 · pith_short_8: H7UOFT2P

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/H7UOFT2PB4PR5BP5PAIOSDJZVE \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3fe8e2cf4f0f1f1e85fd7810e90d39a904359874525ea4ace9a4f98a1532972a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0624228f4d48db660394bcda8cbf3e43c27fd40ffae5dd734b37a9040ec63c4a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-13T22:39:47Z",
    "title_canon_sha256": "59ab018184507f407991cc46a6490ea606e504966409877f12d8cdf61fc9ae62"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14165",
    "kind": "arxiv",
    "version": 1
  }
}