Clustered File Type Identification
Abstract
This thesis examines the possibility of expanding the current field of research
for file type identification in Digital Forensics. A proposed solution is presented
where unsupervised clustering and supervised classification are combined. The
experimentation of the proposed solution increases the speed of file type identification,
however with a decrease of total identification accuracy. A technique of
unsupervised continuous learning is also presented, effectively making the proposed
solution capable of adapting to the environment by learning from the test
data while performing file type identification. In the best case scenario, identification
accuracy increases from 85.8% to 90.4% when using the unsupervised
continuous learning technique.
Description
Masteroppgave informasjons- og kommunikasjonsteknologi - Universitetet i Agder, 2015