Abstract
It is increasingly common in forensic investigations to use automated pre-processing techniques to reduce the massive volumes of data that are encountered. This is typically accomplished by comparing fingerprints (typically cryptographic hashes) of files against existing databases. In addition to finding exact matches of cryptographic hashes, it is necessary to find approximate matches corresponding to similar files, such as different versions of a given file.
This paper presents a new stand-alone similarity hashing approach called saHash, which has a modular design and operates in linear time. saHash is almost as fast as SHA-1 and more efficient than other approaches for approximate matching. The similarity hashing algorithm uses four sub-hash functions, each producing its own hash value. The four sub-hashes are concatenated to produce the final hash value. This modularity enables sub-hash functions to be added or removed, e.g., if an exploit for a sub-hash function is discovered. Given the hash values of two byte sequences, saHash returns a lower bound on the number of Levenshtein operations between the two byte sequences as their similarity score. The robustness of saHash is verified by comparing it with other approximate matching approaches such as +sdhash+.
Keywords
- Fuzzy hashing
- similarity digest
- Levenshtein distance
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References
F. Breitinger, Security Aspects of Fuzzy Hashing, M.Sc. Thesis, Department of Computer Science, Darmstadt University of Applied Sciences, Darmstadt, Germany, 2011.
F. Breitinger, K. Astebol, H. Baier and C. Busch, mvhash-b – A new approach for similarity preserving hashing, Proceedings of the Seventh International Conference on IT Security Incident Management and IT Forensics, pp. 33–44, 2013.
F. Breitinger and H. Baier, Performance issues about context-triggered piecewise hashing, Proceedings of the Third International ICST Conference on Digital Forensics and Cyber Crime, pp. 141–155, 2011.
F. Breitinger and H. Baier, Security aspects of piecewise hashing in computer forensics, Proceedings of the Sixth International Conference on IT Security Incident Management and IT Forensics, pp. 21–36, 2011.
F. Breitinger and H. Baier, A fuzzy hashing approach based on random sequences and Hamming distance, Proceedings of the Conference on Digital Forensics, Security and Law, 2012.
F. Breitinger and H. Baier, Similarity preserving hashing: Eligible properties and a new algorithm mrsh-v2, Proceedings of the Fourth International ICST Conference on Digital Forensics and Cyber Crime, 2012.
F. Breitinger, G. Stivaktakis and H. Baier, FRASH: A framework to test algorithms for similarity hashing, Digital Investigation, vol. 10(S), pp. S50–S58, 2013.
L. Chen and G. Wang, An efficient piecewise hashing method for computer forensics, Proceedings of the First International Workshop on Knowledge Discovery and Data Mining, pp. 635–638, 2008.
N. Harbour, dcfldd ( http://dcfldd.sourceforge.net ), 2006.
P. Jaccard, Distribution de la flore alpine dans le bassin des drouces et dans quelques regions voisines, Bulletin de la Société Vaudoise des Sciences Naturelles, vol. 37(140), pp. 241–272, 1901.
J. Kornblum, Identifying almost identical files using context triggered piecewise hashing, Digital Investigation, vol. 3(S), pp. S91–S97, 2006.
V. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, Soviet Physics Doklady, vol. 10(8), pp. 707–710, 1966.
National Institute of Standards and Technology, National Software Reference Library, Gaithersburg, Maryland ( www.nsrl.nist.gov ).
National Institute of Standards and Technology, Secure Hash Standard (SHS), FIPS Publication 180-4, Gaithersburg, Maryland, 2012.
M. Rabin, Fingerprinting by Random Polynomials, Technical Report TR-15-81, Center for Research in Computing Technology, Harvard University, Cambridge, Massachusetts, 1981.
R. Rivest, The MD5 Message-Digest Algorithm, RFC 1321, 1992.
V. Roussev, Building a better similarity trap with statistically improbable features, Proceedings of the Forty-Second Hawaii International Conference on System Sciences, 2009.
V. Roussev, Data fingerprinting with similarity digests, in Advances in Digital Forensics VI, K. Chow and S. Shenoi (Eds.), Springer, Heidelberg, Germany, pp. 207–226, 2010.
V. Roussev, An evaluation of forensic similarity hashes, Digital Investigation, vol. 8(S), pp. S34–S41, 2011.
V. Roussev, Managing terabyte-scale investigations with similarity digests, in Advances in Digital Forensics VIII, G. Peterson and S. Shenoi (Eds.), Springer, Heidelberg, Germany, pp. 19–34, 2012.
K. Seo, K. Lim, J. Choi, K. Chang and S. Lee, Detecting similar files based on hash and statistical analysis for digital forensic investigations, Proceedings of the Second International Conference on Computer Science and its Applications, 2009.
A. Tridgell, spamsum ( http://mirror.linux.org.au/linux.conf.au/2004/papers/junkcode/spamsum/README ), 2002.
G. Ziroff, Approaches to Similarity-Preserving Hashing, B.Sc. Thesis, Department of Computer Science, Darmstadt University of Applied Sciences, Darmstadt, Germany, 2012.
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© 2014 IFIP International Federation for Information Processing
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Breitinger, F., Ziroff, G., Lange, S., Baier, H. (2014). Similarity Hashing Based on Levenshtein Distances. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics X. DigitalForensics 2014. IFIP Advances in Information and Communication Technology, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44952-3_10
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DOI: https://doi.org/10.1007/978-3-662-44952-3_10
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