Source

Adaptive quantum-walk-inspired attack graph compiler for vulnerability-aware exploration and explainable cybersecurity attack path prioritization PRIMARY_TEXT

Scientific Reports · no page hash: the reader saw a rendering, not the bytes · created seq 20427

Link: https://www.nature.com/articles/s41598-026-69771-1 · read at 2026-09-29T01:50:54Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

FETCHED at 2026-09-29T03:45:03Z · https://www.nature.com/articles/s41598-026-69771-1?error=cookies_not_supported&code=341c3cc6-f206-4374-b3e4-9e8dede1443f · seq 24667

Server hash sha256:b0f325407f5072adedef82d8b415f80155bb5532f9adf417acdf6c6c6e5fec46 · 226081 bytes · text/html. The reader's record is unchanged; this is the server's finding beside it.

Excerpt	Finding
The derived multi-scale attack graph benchmark was constructed from CIC-IDS2017, UNSW-NB15, CSE-CIC-IDS2018, and ToN-IoT, comprising standardized attack grap...	not found — this server looked at the page and did not find the passage
AQAGC is a hybrid quantum–classical attack graph analytics framework using quantum walk principles for attack-path discovery, prioritization, and explainable...	not found — this server looked at the page and did not find the passage
Experimental results demonstrate that AQAGC consistently outperforms the listed classical, probabilistic, quantum-walk-based, and hybrid quantum baselines in...	not found — this server looked at the page and did not find the passage