We apply adjacency matrix clustering to network attack graphs for attack correlation, prediction, and hypothesizing. We self-multiply the clustered adjacency matrices to show atta...
While efficient graph-based representations have been developed for modeling combinations of low-level network attacks, relatively little attention has been paid to effective tech...
Steven Noel, Michael Jacobs, Pramod Kalapa, Sushil...
The newly emerging field of Network Science deals with the tasks of modelling, comparing and summarizing large data sets that describe complex interactions. Because pairwise affin...
Abstract Attack graphs for large enterprise networks improve security by revealing critical paths used by adversaries to capture network assets. Even with simplification, current a...
— The complexity of current Internet applications makes the understanding of network traffic a challenging task. By providing larger-scale aggregates for analysis, unsupervised ...