Discovery and regeneration of hidden emails
Proceedings of the 2005 ACM symposium on Applied computing
Scalable discovery of hidden emails from large folders
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detecting file fragmentation point using sequential hypothesis testing
Digital Investigation: The International Journal of Digital Forensics & Incident Response
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Reassembly of fragmented objects from a collection ofrandomly mixed fragments is a common problem in classicalforensics. In this paper we address the digital forensicequivalent, i.e., reassembly of document fragments, usingstatistical modelling tools applied in data compression. Wepropose a general process model for automatically analyzinga collection fragments to reconstruct the original documentby placing the fragments in proper order. Probabilitiesare assigned to the likelihood that two given fragments areadjacent in the original using context modelling techniquesin data compression. The problem of finding the optimalordering is shown to be equivalent to finding a maximumweight Hamiltonian path in a complete graph. Heuristicsare designed and explored and implementation results providedwhich demonstrate the validity of the proposed technique.