Document Type

Article

Publication Date

5-2014

Subject: LCSH

Computer forensics, Cyber forensics, Hashing (Computer science)

Disciplines

Computer Engineering | Computer Sciences | Electrical and Computer Engineering | Forensic Science and Technology | Information Security

Abstract

Investigating seized devices within digital forensics gets more and more difficult due to the increasing amount of data. Hence, a common procedure uses automated file identification which reduces the amount of data an investigator has to look at by hand. Besides identifying exact duplicates, which is mostly solved using cryptographic hash functions, it is also helpful to detect similar data by applying approximate matching.

Let x denote the number of digests in a database, then the lookup for a single similarity digest has the complexity of O(x). In other words, the digest has to be compared against all digests in the database. In contrast, cryptographic hash values are stored within binary trees or hash tables and hence the lookup complexity of a single digest isO(log2(x)) or O(1), respectively.

In this paper we present and evaluate a concept to extend existing approximate matching algorithms, which reduces the lookup complexity from O(x) to O(1). Therefore, instead of using multiple small Bloom filters (which is the common procedure), we demonstrate that a single, huge Bloom filter has a far better performance. Our evaluation demonstrates that current approximate matching algorithms are too slow (e.g., over 21 min to compare 4457 digests of a common file corpus against each other) while the improved version solves this challenge within seconds. Studying the precision and recall rates shows that our approach works as reliably as the original implementations. We obtain this benefit by accuracy–the comparison is now a file-against-set comparison and thus it is not possible to see which file in the database is matched.

Comments

ª 2014 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/3.0/).

DOI

10.1016/j.diin.2014.03.001

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publisher Citation

Breitinger, Frank; Baier, Harald; White, Douglas (2014): On the database lookup problem of approximate matching. In: Digital Investigation, 11, Supplement 1 (0), pp. S1–S9, 2014, ISSN: 1742-2876, (Proceedings of the First Annual DFRWS Europe).

 
 

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