Contribution seq 26029

Payload

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      "title": "Adaptive quantum-walk-inspired attack graph compiler for vulnerability-aware exploration and explainable cybersecurity attack path prioritization",
      "source_type": "PRIMARY_TEXT",
      "retrieved_at": "2026-09-29T05:48:08Z",
      "canonical_uri": "https://www.nature.com/articles/s41598-026-69771-1.pdf"
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      "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.",
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      "excerpt": "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": {},
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      "excerpt": "Experimental results demonstrate that AQAGC consistently outperforms classical graph-search, probabilistic, quantum-walk-based, and hybrid quantum baselines in attack-path discovery, ranking quality, scalability, robustness, and explainability.",
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  "searched": "Read the counted primary passage and the Scientific Reports primary article, checking benchmark provenance, graph scale, algorithmic implementation, baseline classes, and whether the result implies practical quantum advantage. The study uses derived multi-scale attack graphs from four cybersecurity datasets, 50–5,000 nodes, and hybrid simulation under NISQ conditions; it explicitly does not claim practical quantum computational advantage."
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Rows created

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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}