David Knapp

David Knapp, Johannes Albrecht Holke, Thomas Spenke et al.

The forest-of-refinement-trees approach allows for dynamic adaptive mesh refinement (AMR) at negligible cost. While originally developed for quadrilateral and hexahedral elements, previous work established the theory and algorithms for unstructured meshes of simplicial and prismatic elements. To harness the full potential of tree-based AMR for three-dimensional mixed-element meshes, this paper introduces the pyramid as a new functional element type; its primary purpose is to connect tetrahedral and hexahedral elements without hanging edges. We present a well-defined space-filling curve (SFC) for the pyramid and detail how the unique challenges on the element and forest level associated with the pyramidal refinement are resolved. We propose the necessary functional design and generalize the fundamental global parallel algorithms for refinement, coarsening, partitioning, and face ghost exchange to fully support this new element. Our demonstrations confirm the efficiency and scalability of this complete, hybrid-element dynamic AMR framework.

Justin Allen, David Knapp, Kristine Monteith

The ability to identify applications based on the network data they generate could be a valuable tool for cyber defense. We report on a machine learning technique capable of using netflow-like features to predict the application that generated the traffic. In our experiments, we used ground-truth labels obtained from host-based sensors deployed in a large enterprise environment; we applied random forests and multilayer perceptrons to the tasks of browser vs. non-browser identification, browser fingerprinting, and process name prediction. For each of these tasks, we demonstrate how machine learning models can achieve high classification accuracy using only netflow-like features as the basis for classification.