skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Using 3D Network Visualization to Enhance Rapid Threat Recognition and Response Images.

Abstract

Abstract not provided.

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1122365
Report Number(s):
SAND2005-2598C
505003
DOE Contract Number:
DE-AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: DHS R&D Conference held April 26-28, 2005 in Boston, MA.; Related Information: Proposed for presentation at the DHS R&D Conference held April 26-28, 2005 in Boston, MA.
Country of Publication:
United States
Language:
English

Citation Formats

Custer, Ryan P., Lee, Erik J., and Van Randwyk, Jamie A.. Using 3D Network Visualization to Enhance Rapid Threat Recognition and Response Images.. United States: N. p., 2005. Web.
Custer, Ryan P., Lee, Erik J., & Van Randwyk, Jamie A.. Using 3D Network Visualization to Enhance Rapid Threat Recognition and Response Images.. United States.
Custer, Ryan P., Lee, Erik J., and Van Randwyk, Jamie A.. Fri . "Using 3D Network Visualization to Enhance Rapid Threat Recognition and Response Images.". United States. doi:.
@article{osti_1122365,
title = {Using 3D Network Visualization to Enhance Rapid Threat Recognition and Response Images.},
author = {Custer, Ryan P. and Lee, Erik J. and Van Randwyk, Jamie A.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 01 00:00:00 EST 2005},
month = {Fri Apr 01 00:00:00 EST 2005}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • No abstract prepared.
  • No abstract prepared.
  • The objectives of this work include automatic recovery and visualization of a 3D chromosome structure from a sequence of 2D tomographic reconstruction images taken through the nucleus of a cell. Structure is very important for biologists as it affects chromosome functions, behavior of the cell and its state. Chromosome analysis is significant in the detection of deceases and in monitoring environmental gene mutations. The algorithm incorporates thresholding based on a histogram analysis with a polyline splitting algorithm, contour extraction via active contours, and detection of the 3D chromosome structure by establishing corresponding regions throughout the slices. Visualization using point cloudmore » meshing generates a 3D surface. The 3D triangular mesh of the chromosomes provides surface detail and allows a user to interactively analyze chromosomes using visualization software.« less
  • Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated tool support in the effort to make their tasks more efficient, accurate, and timely. This tool support comes in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, often speak of a need for tools which help them merge the data they already have and help them establish appropriate baselines against which to compare potential anomalies. Furthermore, existing threat modelsmore » that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand they analytic process and how one such model, the MITRE ATT&CK Matrix [32], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into the threat model to inform our eventual visualization designs. We examine data mapping for gaps where the threat model is under-supported by either data or tools. We discuss these gaps as potential design spaces for future research efforts. We also discuss the design of a prototype tool that combines machine-learning and visualization components to support cyber analysts working with this threat model.« less