Clustering similarity digest bloom filters in self-organizing maps

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...

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Bibliographic Details
Main Author:	Delaroderie, John C.
Other Authors:	Young, Joel
Published:	Monterey, California. Naval Postgraduate School 2013
Online Access:	http://hdl.handle.net/10945/27820

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spelling	ndltd-nps.edu-oai-calhoun.nps.edu-10945-278202014-11-27T16:17:02Z Clustering similarity digest bloom filters in self-organizing maps Delaroderie, John C. Young, Joel Martell, Craig The Navy 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. 2013-02-15T23:13:31Z 2013-02-15T23:13:31Z 2012-12 Thesis http://hdl.handle.net/10945/27820 Approved for public release; distribution is unlimited. Monterey, California. Naval Postgraduate School
collection	NDLTD
sources	NDLTD
description	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.
author2	Young, Joel
author_facet	Young, Joel Delaroderie, John C.
author	Delaroderie, John C.
spellingShingle	Delaroderie, John C. Clustering similarity digest bloom filters in self-organizing maps
author_sort	Delaroderie, John C.
title	Clustering similarity digest bloom filters in self-organizing maps
title_short	Clustering similarity digest bloom filters in self-organizing maps
title_full	Clustering similarity digest bloom filters in self-organizing maps
title_fullStr	Clustering similarity digest bloom filters in self-organizing maps
title_full_unstemmed	Clustering similarity digest bloom filters in self-organizing maps
title_sort	clustering similarity digest bloom filters in self-organizing maps
publisher	Monterey, California. Naval Postgraduate School
publishDate	2013
url	http://hdl.handle.net/10945/27820
work_keys_str_mv	AT delaroderiejohnc clusteringsimilaritydigestbloomfiltersinselforganizingmaps
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Clustering similarity digest bloom filters in self-organizing maps

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