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Hash-based file content identification using distributed systems

: Yannikos, Y.; Schluessler, J.; Steinebach, M.; Winter, C.; Graffi, K.


Peterson, G. ; International Federation for Information Processing -IFIP-, Working Group Digital Forensics:
Advances in digital forensics IX. 9th IFIP WG 11.9 International Conference on Digital Forensics : Orlando, FL, USA, January 28 - 30, 2013; revised selected papers
Berlin: Springer, 2013 (IFIP advances in information and communication technology 410)
ISBN: 3-642-41147-9
ISBN: 978-3-642-41147-2 (Print)
ISBN: 978-3-642-41148-9 (Online)
International Conference on Digital Forensics <9, 2013, Orlando/Fla.>
Fraunhofer SIT ()

A major challenge in digital forensics is the handling of very large amounts of data. Since forensic investigators often have to analyze several terabytes of data in a single case, efficient and effective tools for automatic data identification and filtering are required. A common data identification technique is to match the cryptographic hashes of files with hashes stored in blacklists and whitelists in order to identify contraband and harmless content, respectively. However, blacklists and whitelists are never complete and they miss most of the files encountered in investigations. Also, cryptographic hash matching fails when file content is altered even very slightly. This paper analyzes several distributed systems for their ability to support file content identification. A framework is presented for automated file content identification that searches for file hashes and collects, aggregates and presents the search results. Experiments demonstrate that the framework can provide identifying information for 26% of the test files from their hashed content, helping reduce the workload of forensic investigators.