An efficient approach to assessing the risk of zero-day vulnerabilities

Computer systems are vulnerable to both known and zero-day attacks. Although known attack patterns can be easily modeled, thus enabling the development 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 suitable 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 set 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 through experiments, and show that the proposed algorithms are computationally efficient and accurate.