0000000000276815
AUTHOR
Simone Sante Ruffo
A bio-inspired approach to attack graphs analysis
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 graphs analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building…
BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture
Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault…