A queueing framework segments vulnerability data with Gaussian mixture models, fits arrival/service/resource parameters by KL-divergence minimization, and reports 91-96% accuracy in estimating organizational cyber resources from timestamps.
An attack surface metric,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A queueing model of attack surfaces validated on supply-chain data shows AI automation can raise exploit rates and an RL policy cuts active vulnerabilities by over 90% without extra budget.
citing papers explorer
-
Organizational Security Resource Estimation via Vulnerability Queueing
A queueing framework segments vulnerability data with Gaussian mixture models, fits arrival/service/resource parameters by KL-divergence minimization, and reports 91-96% accuracy in estimating organizational cyber resources from timestamps.
-
A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense
A queueing model of attack surfaces validated on supply-chain data shows AI automation can raise exploit rates and an RL policy cuts active vulnerabilities by over 90% without extra budget.