A B S T R A C T
This research proposes eighteen quantifiable characteristics of allocated files, unallocated
clusters, and string search hits contained therein, which can be used to relevancy rank
string search output. We executed a 36-term query across four disks in a synthetic case
(“M57 Patents” from DigitalCorpora.org), which produced over two million search hits
across nearly 50,000 allocated files and unallocated clusters. We sampled 21,400 search
hits from the case, extracted the proposed feature values, trained binary class (relevant/
not-relevant) support vector machine (SVM) models, derived two relevancy ranking
functions from the resultant model feature weights, and empirically tested the ranking
algorithms. We achieved 81.02% and 85.97% prediction accuracies for the allocated and
unallocated models, respectively. Further research is needed to validate these algorithms
in a broader set of real-world cases, and/or adapt the algorithms to improve their
robustness. Nonetheless, this research provides an important starting point for research
into digital forensic search hit relevancy ranking algorithms. We proposed an initial set of
relevancy ranking features and obtained very promising empirical results. The ability to
achieve rank-ordered list output for search queries in digital forensics, similar to what web
browsing and digital library users enjoy, is extremely important for digital forensic practitioners
to reduce the analytical burden of text string searching e a valuable analytical
technique.
© 2014 Digital Forensics ResearchWorkshop. Published by Elsevier Ltd. All rights reserved.
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کد محصول : شماره 121
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