Bio-inspired security analysis for IoT scenarios

Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory to some IoT scenarios as use cases are also outlined.