Cyberspace Math Models

Abstract

The goal of this effort was to explore ways of characterizing the complexity, performance, vulnerability, and dynamic properties of networks and complex systems. Techniques investigated included information theory measures and stochastic resonance, together with graphical and statistical assessments. Network persistance was characterized by the Hurst parameter in a network model based on fractional brownian motion, generalized by fast Fourier transform and studied with wavelet analysis. Network uncertainty was characterized with approximate entropy. General applicability to other complex systems were studied in the areas of sunspot cycles, chatbot detection, genetic data, and image processing.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA589817

Entities

People

  • Daniel W. Repperger
  • David J. Rieksts
  • Fairul Mohd-zaid
  • John P. McIntire
  • Katheryn A. Farris
  • Leslie M. Blaha
  • Lyndsey Mcintire
  • Paul R. Havig
  • Russell Francis
  • Xiaoping Shen

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Air Force
  • Complex Systems
  • Computational Science
  • Computer Networks
  • Databases
  • Detection
  • Diseases And Disorders
  • Electronic Mail
  • Genetics
  • Image Processing
  • Information Processing
  • Information Science
  • Information Theory
  • Network Science
  • Random Variables
  • Reasoning
  • Signal Processing

Readers

  • Cybersecurity.
  • Image Processing and Computer Vision.
  • Statistical inference.

Technology Areas

  • Biotechnology
  • Cyber
  • Cyber - Cryptography