A Machine Learning-Based Approach to Digital Triage

Dealing with machine learning-based digital triage, this chapter presents a framework for selective pre-examination and statistical classification of digital data sources that may be deployed both on the crime scene and at Digital Forensic Laboratory (DFLs). This framework provides investigators with quick actionable intelligence on the crime scene when time is a critical factor. It also protects a suspect's privacy where excessive search and seizure of data is not allowed by the legal system. The framework presents two main advantages with respect to most examination techniques in use today as it requires limited manual intervention and produces measurable and reproducible error rates. The chapter presents an overview about the history and state-of-the-art research on digital triage. The chapter presents a case study on mobile phones classification in court cases of child pornography exchange. It concludes with a discussion on the challenges and future directions for the digital forensics community.