An Embedded Bayesian Network Hidden Markov Model for Digital Forensics

In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov Model (EBN-HMM) for tracking and predicting the degree of criminal activity as it evolves over time. The model is evaluated with 500 randomly produced digital forensic scenarios and two specific forensic cases. The experimental results indicate that the model fits well with expert classification of forensic data. Such initial results point out the potential of such Dynamical Bayesian Network methods for the analysis of digital forensic data.

Authors and Affiliations

1. Defence Science & Technology Organisation (DSTO), Australia

   Olivier De Vel

2. National ICT Australia (NICTA),  

   Nianjun Liu, Terry Caelli & Tiberio S. Caetano

Editors and Affiliations

1. School of Information and Computer Science, University of California, Irvine

   Sharad Mehrotra

2. MIS Department, University of Arizona, 85721, Tucson, AZ, USA

   Daniel D. Zeng

3. Department of Management Information Systems, Eller College of Management, The University of Arizona, 85721, AZ, USA

   Hsinchun Chen

4. University of Texas at Dallas,  

   Bhavani Thuraisingham

5. Chinese Academy of Sciences, 100190, Beijing, China

   Fei-Yue Wang

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