The digital information systems have become increasingly complex and inextricably intertwined with the infrastructure of national, public, and private organizations. The forensic digital analysis as a whole, in its relative infancy, is the unwilling victim of the rapid advancement of computer technology, so it is at the mercy of ever more new and complex computing approaches. Forensic digital analysis is unique among the forensic sciences in that it is inherently mathematical and generally comprises more data from an investigation than is present in other types of forensics. The digital investigation process can be driven using numerous forensic investigation models. Among these is the need to analyze forensic materials over complex chains of evidence in a wide variety of heterogeneous computing platforms. The current computer forensic investigation paradigm is laborious and requires significant expertise on the part of the investigators. This paper presents the application of JDL data fusion model in computer forensics for analyzing the information from seized hard drives along with an analysis of the interpreted information to prove that the respective user has misused internet. This paper is an attempt to use the data fusion and decision mining processes, to help in enhancing
the quality of the investigation process which is in turn is validated by statistical evaluation. The mining rules generation process is based on the decision tree as a classification method to study the main attributes that may help in detecting the suspicious behavior. A system that facilitates the use of the generated rules is built which allows investigating agencies to predict the suspicious behavior under study.
Computer forensic, Digital Investigation, Digital evidence, Cyber Crime, Data Fusion, Decision mining.
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THE INTERNATIONAL JOURNAL OF FORENSIC COMPUTER SCIENCE - IJoFCS
Volume 7, Number 1, pages 16-23, DOI: 10.5769/J201201002 or http://dx.doi.org/10.5769/J201201002
Application of data fusion methodology for computer forensics dataset analysis to resolve data quality issues in predictive digital evidence
By Suneeta Satpathy, Sateesh Pradhan, and B. B. Ray
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