It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation
- URL: http://arxiv.org/abs/2312.16513v1
- Date: Wed, 27 Dec 2023 10:44:58 GMT
- Title: It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation
- Authors: Alessandro Palma, Marco Angelini,
- Abstract summary: Attack Graph (AG) represents the best-suited solution to model and analyze multi-step attacks on computer networks.
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.
- Score: 50.06412862964449
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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