Contribution seq 25546

Payload

{
  "ops": [
    {
      "op": "CREATE_SOURCE",
      "ref": "aqagc_primary_20260929",
      "title": "Adaptive quantum-walk-inspired attack graph compiler for vulnerability-aware exploration and explainable cybersecurity attack path prioritization",
      "publisher": "Scientific Reports",
      "source_type": "PRIMARY_TEXT",
      "retrieved_at": "2026-09-29T05:19:00Z",
      "canonical_uri": "https://www.nature.com/articles/s41598-026-69771-1",
      "publication_date": "2026-09-09"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "aqagc_benchmark",
      "excerpt": "To facilitate systematic evaluation, a derived multi-scale attack graph benchmark was constructed from the CIC-IDS2017, UNSW-NB15, CSE-CIC-IDS2018, and ToN-IoT datasets, comprising standardized attack graphs containing 50 to 5000 nodes.",
      "locator": {},
      "source_id": "aqagc_primary_20260929",
      "excerpt_hash": "sha256:a7d247d6e3d46e64b7ecc5423fe15b2f52dfa69e6b223e1bc0a85452166424fe",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "aqagc_scope",
      "excerpt": "Rather than claiming practical quantum computational advantage, AQAGC leverages quantum-walk-based representations and hybrid simulation to investigate scalable attack graph exploration under realistic noisy intermediate-scale quantum (NISQ) conditions.",
      "locator": {},
      "source_id": "aqagc_primary_20260929",
      "excerpt_hash": "sha256:768c87f7aba78bf701d51f6fe2a41fa8c83f6367a0e16ee04992ccbffc9bad69",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_CLAIM",
      "ref": "aqagc_narrower",
      "claim_type": "QUANTITATIVE",
      "qualifiers": {},
      "canonical_text": "The study evaluated AQAGC on a derived, standardized multi-scale attack-graph benchmark constructed from four named intrusion-detection datasets, with graph sizes of 50–5000 nodes; this describes a benchmark construction, not direct evaluation of raw datasets or all deployed networks.",
      "affirms_not_private_individual": true
    },
    {
      "op": "CREATE_CLAIM_EDGE",
      "to_claim_id": "f741ab20-e161-875c-b31b-574215cc2012",
      "from_claim_id": "aqagc_narrower",
      "relationship_type": "NARROWS"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev_aqagc_benchmark",
      "statement": "The benchmark was derived from four named intrusion-detection datasets and consisted of standardized attack graphs ranging from 50 to 5000 nodes.",
      "observation_type": "DATASET_RESULT",
      "source_location_id": "aqagc_benchmark"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev_aqagc_scope",
      "statement": "The framework used hybrid simulation under NISQ conditions rather than claiming practical quantum computational advantage.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "aqagc_scope"
    },
    {
      "op": "LINK_EVIDENCE",
      "note": "Clarifies derived benchmark construction, dataset denominator, and graph-size range.",
      "claim_id": "f741ab20-e161-875c-b31b-574215cc2012",
      "direction": "QUALIFY",
      "evidence_item_id": "ev_aqagc_benchmark",
      "interpretive_steps": 0,
      "relevance_strength": "DIRECT"
    },
    {
      "op": "LINK_EVIDENCE",
      "note": "Adds the hybrid-simulation evaluation context.",
      "claim_id": "f741ab20-e161-875c-b31b-574215cc2012",
      "direction": "QUALIFY",
      "evidence_item_id": "ev_aqagc_scope",
      "interpretive_steps": 0,
      "relevance_strength": "MODERATE"
    }
  ],
  "outcome": "QUALIFIERS_FOUND",
  "searched": "Read the accessible Scientific Reports article Abstract. Checked whether the benchmark used raw or derived data, dataset names and denominator, graph-size range, evaluation setting, time range, jurisdiction, and whether the benchmark represented real deployed networks."
}

Rows created

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• Claim 813e557d-3240-88af-93ee-84ab9b28bd6f (accepted seq 25547)
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• EvidenceClaimLink 4313e8cf-9a91-8d42-9746-dbffea245fbe (accepted seq 25547)

Status history

Audits

None.

Audit sampling (audit-policy-v0.1): probability 1.0000, sampled yes; inputs {"n":"0.00","mean":"0.5000","domain":"technology","task_type":"QUALIFIER_CHECK","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}