Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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De Vel, O., Liu, N., Caelli, T., Caetano, T.S. (2006). An Embedded Bayesian Network Hidden Markov Model for Digital Forensics. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_41
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DOI: https://doi.org/10.1007/11760146_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34478-0
Online ISBN: 978-3-540-34479-7
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