A Generic Bayesian Belief Model for Similar Cyber Crimes

Bayesian belief network models designed for specific cyber crimes can be used to quickly collect and identify suspicious data that warrants further investigation. While Bayesian belief models tailored to individual cases exist, there has been no consideration of generalized case modeling. This paper examines the generalizability of two case-specific Bayesian belief networks for use in similar cases. Although the results are not conclusive, the changes in the degrees of belief support the hypothesis that generic Bayesian network models can enhance investigations of similar cyber crimes.

[1]  Fred Cohen Two Models of Digital Forensic Examination , 2009, 2009 Fourth International IEEE Workshop on Systematic Approaches to Digital Forensic Engineering.

[2]  Sujeet Shenoi,et al.  Advances in Digital Forensics VIII , 2012, IFIP Advances in Information and Communication Technology.

[3]  Wanlei Zhou,et al.  An Analytical Model for DDoS Attacks and Defense , 2006, 2006 International Multi-Conference on Computing in the Global Information Technology - (ICCGI'06).

[4]  Marcus K. Rogers,et al.  Computer Forensics Field Triage Process Model , 2006, J. Digit. Forensics Secur. Law.

[5]  Indrajit Ray,et al.  Advances in Digital Forensics IV , 2008 .

[6]  Richard E. Overill,et al.  A Cost-Effective Model for Digital Forensic Investigations , 2009, IFIP Int. Conf. Digital Forensics.

[7]  Sujeet Shenoi,et al.  Advances in Digital Forensics V - Fifth IFIP WG 11.9 International Conference on Digital Forensics, Orlando, Florida, USA, January 26-28, 2009, Revised Selected Papers , 2009, IFIP Int. Conf. Digital Forensics.

[8]  Kam-Pui Chow,et al.  Reasoning About Evidence Using Bayesian Networks , 2012, IFIP Int. Conf. Digital Forensics.

[9]  Jantje A. M. Silomon,et al.  Digital Meta-Forensics : Quantifying the Investigation , 2010 .

[10]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[11]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[12]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[13]  Richard E. Overill,et al.  Proc. 4th International Conference on Cybercrime Forensics Education & Training (CFET 2010) , 2010 .