Automated generation of attack trees by unfolding graph transformation systems
David Huistra · 2016
Analysis of an organization's security and the threats it faces is nowadays often done using attack trees that describe all possible threats facing a system or organization. A big challenge lies in obtaining these attack trees. Manually constructing them is tedious and error-prone work. Therefore, this project focuses on generating attack trees automatically from a given model that describes a system or organization. It improves upon previous efforts by providing an approach to identify all possible attacks from a given model in a more scalable manner, compared to the previous approach of constructing an attack graph, while remaining (security-)domain independent. This work demonstrates that this new approach, based on partial-order reduction, can have significant scalability benefits compared to the existing generic approach. In addition, it is shown that the graph transformations modeling paradigm can be used as a generic input language for describing systems and organizations, and using graph transformations gives the benefit of reusing existing efforts and implementations. Specifically, a partial-order technique called the unfolding of a graph transformation system is used as the basis of the approach, and GROOVE, a tool for constructing and analyzing graph transformation systems, is used as the basis of the implementation.