Abstract: This article presents a percolation-based approach for the analysis of risk propagation, using malware spreading as a showcase example. Conventional risk management is often driven by human (subjective) assessment of how one risk influences the other, respectively, how security incidents can affect subsequent problems in interconnected (sub)systems of an infrastructure. Using percolation theory, a well-established methodology in the fields of epidemiology and disease spreading, a simple simulation-based method is described to assess risk propagation system-atically. This simulation is formally analyzed using percolation theory, to obtain closed form criteria that help predicting a pandemic incident propagation (or a propagation with average-case bounded implications). The method is designed as a security decision support tool, e.g., to be used in security operation centers. For that matter, a flexible visualization technique is devised, which is naturally induced by the percolation model and the simulation algorithm that derives from it. The main output of the model is a graphical visualization of the infrastructure (physical or logical topology). This representation uses color codes to indicate the likelihood of problems to arise from a security incident that initially occurs at a given point in the system. Large likelihoods for problems thus indicate “hotspots”, where additional action should be taken.

Sandra Konig, Stefan Rass, Stefan Schauer and Alexander Beck, “Risk Propagation Analysis and Visualization using Percolation Theory” International Journal of Advanced Computer Science and Applications(ijacsa), 7(1), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070194

@article{Konig2016,
title = {Risk Propagation Analysis and Visualization using Percolation Theory},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070194},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070194},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {1},
author = {Sandra Konig and Stefan Rass and Stefan Schauer and Alexander Beck}
}