Computer systems are vulnerable to both known and zero-day attacks. Although known attack patterns can be easily modeled, thus enabling the definition of suitable hardening strategies, handling zero-day vulnerabilities is inherently difficult due to their unpredictable nature. Previous research has... See full abstract

Computer systems are vulnerable to both known and zero-day attacks. Although known attack patterns can be easily modeled, thus enabling the definition of suitable hardening strategies, handling zero-day vulnerabilities is inherently difficult due to their unpredictable nature. Previous research has attempted to assess the risk associated with unknown attack patterns, and a metric to quantify such risk, the k-zero-day safety metric, has been defined. However, existing algorithms for computing this metric are not scalable, and assume that complete zero-day attack graphs have been generated, which may be unfeasible in practice for large networks. In this paper, we propose a framework comprising a suite of polynomial algorithms for estimating the k-zero-day safety of possibly large networks efficiently, without pre-computing the entire attack graph. We validate our approach experimentally, and show that the proposed solution is computationally efficient and accurate.
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