  The ever increasing volume of data in digital forensic investigation is one
of the most discussed challenges in the field. Usually, most of the file
artefacts on seized devices are not pertinent to the investigation. Manually
retrieving suspicious files relevant to the investigation is akin to finding a
needle in a haystack. In this paper, a methodology for the automatic
prioritisation of suspicious file artefacts (i.e., file artefacts that are
pertinent to the investigation) is proposed to reduce the manual analysis
effort required. This methodology is designed to work in a human-in-the-loop
fashion. In other words, it predicts/recommends that an artefact is likely to
be suspicious rather than giving the final analysis result. A supervised
machine learning approach is employed, which leverages the recorded results of
previously processed cases. The process of features extraction, dataset
generation, training and evaluation are presented in this paper. In addition, a
toolkit for data extraction from disk images is outlined, which enables this
method to be integrated with the conventional investigation process and work in
an automated fashion.
