Exploring risk flow attack graph for security risk assessment


Researchers have previously looked into the matter of determining the association between invasive events and network risk, and attack graph (AG) was proposed to hunt countermeasures. However, AG has proved to have varied limitations in sensible applications. To overcome such defects, this study presents a risk flow attack graph (RFAG)-based risk assessment approach. In particular, this approach applies a RFAG to represent network and attack eventualities, that are then fed to a network flow model for computing risk flow. A bi-objective sorting algorithm is employed to automatically infer the priority of risk methods and assist risk assessment, and a fuzzy comprehensive analysis is performed to see risk severity. Via the aforementioned processes, the authors simplify AG and follow the chance path of originating, transferring, redistributing and converging to assess security risk. The authors use a synthetic network scenario to illustrate this approach and evaluate its performance through a set of simulations. Experiments show that the approach is capable of effectively identifying network security things and assessing critical risk.

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