It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation
CoRR(2023)
摘要
In modern computer networks where sophisticated cyber attacks occur daily, a
timely cyber risk assessment becomes paramount. Attack Graph (AG) represents
the best-suited solution to model and analyze multi-step attacks on computer
networks, although they suffer from poor scalability due to their combinatorial
complexity. This paper introduces an analysis-driven framework for AG
generation. It enables real-time attack path analysis before the completion of
the AG generation with a quantifiable statistical significance. We further
accelerate the AG generation by steering it with the analysis query and
supporting a novel workflow in which the analyst can query the system anytime.
To show the capabilities of the proposed framework, we perform an extensive
quantitative validation and we present a realistic case study on networks of
unprecedented size. It demonstrates the advantages of our approach in terms of
scalability and fitting to common attack path analyses.
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