Titre
Automated evaluation of approximate matching algorithms on real data
Type
article
Institution
Externe
Périodique
Auteur(s)
Breitinger, Frank
Auteure/Auteur
Roussev, Vassil
Auteure/Auteur
Liens vers les personnes
ISSN
1742-2876
Statut éditorial
Publié
Date de publication
2014-05
Volume
11
Numéro
0
Première page
S10
Dernière page/numéro d’article
S17
Langue
anglais
Résumé
Abstract Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to screen and analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similar representations. Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages and evaluation methodology is still evolving. Broadly, prior approaches have used either a human in the loop to manually evaluate the goodness of similarity matches on real world data, or controlled (pseudo-random) data to perform automated evaluation. This work’s contribution is to introduce automated approximate matching evaluation on real data by relating approximate matching results to the longest common substring (LCS). Specifically, we introduce a computationally efficient {LCS} approximation and use it to obtain ground truth on the t5 set. Using the results, we evaluate three existing approximate matching schemes relative to {LCS} and analyze their performance.
PID Serval
serval:BIB_200E1BD52A3B
Open Access
Oui
Date de création
2021-05-06T10:01:50.073Z
Date de création dans IRIS
2025-05-20T17:42:44Z