An Automated Solution to the Multiuser Carved Data Ascription Problem

Abstract

This paper presents a novel solution to the problem of determining the ownership of carved information found on disk drives and other storage media that have been used by more than one person. When a computer is subject to forensic examination information may be found that cannot be readily ascribed to a specific user. Such information is typically not located in a specific file or directory, but is found through file carving, which recovers data from unallocated disk sectors. Because the data is carved, it does not have associated file system metadata, and its owner cannot be readily ascertained. The technique presented in this paper starts by automatically recovering both file system metadata as well as extended metadata embedded in files (for instance, embedded timestamps) directly from a disk image. This metadata is then used to find exemplars and to create a machine learning classifier that can be used to ascertain the likely owner of the carved data. The resulting classifier is well suited for use in a legal setting since the accuracy can be easily verified using cross-validation. Our technique also results in a classifier that is easily validated by manual inspection. We report results of the technique applied to both specific hard drive data created in our laboratory and multiuser drives that we acquired on the secondary market. We also present a tool set that automatically creates the classifier and performs validation.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2010
Accession Number
ADA549361

Entities

People

  • Aleatha Parker-wood
  • Daniel Huynh
  • James Migletz
  • Simson Garfinkel

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Computational Forensics
  • Computer Crime
  • Computer Programs
  • Computer Science
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Network Science
  • Operating Systems
  • Supervised Machine Learning
  • United States Military Academy
  • Web Browsers
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML