Forensic Activity Classification Using Digital Traces from iPhones: A Machine Learning-based Approach

TL;DR

This paper introduces a machine learning approach to classify physical activities from digital traces on iPhones, aiding forensic investigations by providing likelihood ratios for activity identification.

Contribution

It presents a novel machine learning method to translate iPhone sensor data into likelihood ratios for activity classification, with a new dataset and open-source code for research advancement.

Findings

Successfully distinguished 167 out of 171 activity pairs

Extended approach to analyze multiple activities simultaneously

Created activity timelines to support forensic investigations

Abstract

Smartphones and smartwatches are ever-present in daily life, and provide a rich source of information on their users' behaviour. In particular, digital traces derived from the phone's embedded movement sensors present an opportunity for a forensic investigator to gain insight into a person's physical activities. In this work, we present a machine learning-based approach to translate digital traces into likelihood ratios (LRs) for different types of physical activities. Evaluating on a new dataset, NFI\_FARED, which contains digital traces from four different types of iPhones labelled with 19 activities, it was found that our approach could produce useful LR systems to distinguish 167 out of a possible 171 activity pairings. The same approach was extended to analyse likelihoods for multiple activities (or groups of activities) simultaneously and create activity timelines to aid in both the early and latter stages of forensic investigations. The dataset and all code required to replicate the results have also been made public to encourage further research on this topic.