Applying topic modeling to forensic data

Abstract

Most actionable evidence is identified during the analysis phase of digital forensic investigations. Currently, the analysis phase uses expression-based searches, which assume a good understanding of the evidence; but latent evidence cannot be found using such methods. Knowledge discovery and data mining (KDD) techniques can significantly enhance the analysis process. A promising KDD technique is topic modeling, which infers the underlying semantic context of text and summarizes the text using topics described by words. This paper investigates the application of topic modeling to forensic data and its ability to contribute to the analysis phase. Also, it highlights the challenges that forensic data poses to topic modeling algorithms and reports on the lessons learned from a case study. © 2008 International Federation for Information Processing.