Research Output

Experimental evaluation of disk sector hash comparison for forensic triage using a Bloom filter.

  There is a problem in the world of digital forensics. The demands on digital forensic investigators and resources will continue to increase as the use of computers and other electronic devices increases, and as the storage capacity of these devices increases. The digital forensic process requires that evidence be identified and examined, and resources to do this are constrained. This is creating a backlog of work as seized media and devices wait to be analysed, and some investigations or checks 'in the field' may be reduced or discarded as impractical. There is a technique which can be used to help quickly to collect and examine data to see if it is of interest. This technique combines statistical sampling and hashes as described by Garfinkel et al (2010). This tool can use a Bloom filter to match the hashes from disk sectors against the stored hashes for a file which is being searched for. The tool was successfully implemented and the Bloom filter false positive rate was as predicted by theory (Roussev, Chen, Bourg, & Richard, 2006) which confirmed that the Bloom filter had been correctly implemented. This tool was written in Python which proved a simple to use programming language. This prototype tool can provide the basis for further work on a practical tool for use in real world digital forensics investigation.

  • Date:

    01 January 2013

  • Publication Status:

    Accepted

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

Citation

Buchanan, W. J., Macfarlane, R., & Clayton, J. (2012). Experimental evaluation of disk sector hash comparison for forensic triage using a Bloom filter. In G. Weir, & M. Daley (Eds.), Cyberforensics Perspectives : Proceedings of the 3rd International Conference on Cybercrime, Security and Digital Forensics (Cyberforensics 2013)

Authors

Keywords

Digital forensics; statistical sampling; hashes; Boom filter;

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