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Adaptive quantum-walk-inspired attack graph compiler for vulnerability-aware exploration and explainable cybersecurity attack path prioritization PRIMARY_TEXT
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FETCHED at 2026-09-29T03:42:03Z · https://www.nature.com/articles/s41598-026-69771-1?error=cookies_not_supported&code=7cf28a95-9c25-40b7-bf13-cf22cc4141b9 · seq 24653
| Excerpt | Finding |
|---|---|
| This paper proposes the Adaptive Quantum-Walk-Inspired Attack Graph Compiler (AQAGC), a hybrid quantum–classical attack graph analytics framework that employ... | normalized — the passage is on the page once typography is folded together |
| A derived multi-scale attack graph benchmark was constructed from the CIC-IDS2017, UNSW-NB15, CSE-CIC-IDS2018, and ToN-IoT datasets, comprising standardized ... | normalized — the passage is on the page once typography is folded together |
| Experimental results demonstrate that AQAGC consistently outperforms classical graph-search, probabilistic, quantum-walk-based, and hybrid quantum baselines ... | verbatim — the passage is on the page, character for character |
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| Locator | Excerpt |
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QUOTE {} | This paper proposes the Adaptive Quantum-Walk-Inspired Attack Graph Compiler (AQAGC), a hybrid quantum–classical attack graph analytics framework that employs quantum walk principles to improve attack-path discovery, prioritization, and ... |
QUOTE {} | 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. |
QUOTE {} | 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 explaina... |