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(Poster) Zero-Day Risk Estimation Using Security Games
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AI
DOI：10.1007/978-3-032-08067-7_18.png)
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Abstract
En 中文
We propose a method using game-theoretic security models and attack graphs to estimate zero-day exploit risks. Our approach predicts risk increases over time or under a presumed dark count of unknown exploits without speculating on their specifics. The method models a game where the defender has a limited view of the attackers full action space, simulating zero-day scenarios. This avoids unreliable guessing of potential attacks and focuses on the attackers knowledge advantage relative to the defender. The approach is generic, requiring only mild computability conditions, and is demonstrated using a prior game-theoretic model applied to industrial robotics case studies, but not limited to such applications (in fact agnostic of the use-case).
Keywords:
zero-day risk
attack-defense simulation
security metrics
network security
attack graph
network hardening
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Papers:
16
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