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Using Normalized Compression Distance for Classifying File Fragments
Responsible organisation
2010 (English)Conference paper, (Refereed) Published
Abstract [en]

We have applied the generalised and universal distance measure NCD-Normalised Compression Distance-to the problem of determining the types of file fragments via example. A corpus of files that can be redistributed to other researchers in the field was developed and the NCD algorithm using k-nearest-neighbour as the classification algorithm was applied to a random selection of file fragments. The experiment covered circa 2000 fragments from 17 different file types. While the overall accuracy of the n-valued classification only improved the prior probability of the class from approximately 6% to circa 50% overall, the classifier reached accuracies of 85%-100% for the most successful file types.

Place, publisher, year, edition, pages
Cracow: IEEE , 2010.
Keyword [en]
FOR-DMIN, APP-IDEN
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-7777DOI: 10.1109/ARES.2010.100ISI: 000278197800098Local ID: oai:bth.se:forskinfoB8C74F34B1A4206FC12577580027FC20ISBN: 978-0-7695-3965-2 (print)OAI: oai:DiVA.org:bth-7777DiVA: diva2:835439
Conference
5th International Conference on Availability, Reliability and Security
Available from: 2012-09-18 Created: 2010-07-06 Last updated: 2015-06-30Bibliographically approved

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Axelsson, Stefan
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Total: 29 hits
CiteExportLink to record
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