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Automated Attack Planning

Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing. A key question then is how to automatically generate the attacks. A natural way to address this issue is as an attack planning problem. In this thesis, we are concerned with the specific context of regular automated pentesting, and use the term "attack planning" in that sense. The following three research directions are investigated. First, we introduce a conceptual model of computer network attacks, based on an analysis of the penetration testing practices. We study how this attack model can be represented in the PDDL language. Then we describe an implementation that integrates a classical planner with a penetration testing tool. This allows us to automatically generate attack paths for real world pentesting scenarios, and to validate these attacks by executing them. Secondly, we present efficient probabilistic planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These alg