Project

The rapid increase in the use of digital devices in the past decade has resulted in significant new challenges for digital forensics. In this context, the goal of this project is to develop new statistical methodologies to support digital forensics in analysis of user-generated event data. The project will focus on native or cloud-based logged event data typically of the form

<accountID, event, timestamp>

coming from devices such as computers and mobile phones.

The types of forensic questions to be addressed will focus on identity-related questions, such as whether two sets of geolocation events were generated by the same individual or by two different individuals. Traditional approaches in statistical forensics, such as likelihood ratios and score-based likelihood ratios, will be extended to handle user-generated event data.

The extensions will require development of robust and accurate new probabilistic models and fast new algorithms for large data sets. The project will use a variety of different real-world user event datasets as testbeds for development and evaluation. See the Datasets section for a list of current datasets.