Clustering Similarity Digest Bloom Filters in Self-Organizing Maps

Abstract

In response to increasing numbers of cases involving digital media, and the increasing sizes of and number of pieces of media in those cases, forensic investigators are relying increasingly on triage techniques for prioritizing which media to review. This thesis describes a framework for clustering documents aquired during a digital forensics investigation on a self organizing (aka Kahonen) map allowing new documents to be categorized relative to existing documents. Furthermore the presented algorithm avoids the need to work with source documents but with sdhash fingerprints allowing a fifty-fold reduction in data required. To test the methodology, document fingerprints are regenerated from the SOM and compared.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2012
Accession Number
ADA576321

Entities

People

  • John C. Delaroderie

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Black Holes
  • Computational Forensics
  • Computational Science
  • Computer Programs
  • Computer Science
  • Computers
  • Governments
  • Information Science
  • Machine Learning
  • Neural Networks
  • Operating Systems
  • Recurrent Neural Networks
  • Standards
  • Test Methods
  • Two Dimensional

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