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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5539

Title: Specializing CRISP-DM for evidence mining
Authors: Venter, JP
De Waal, A
Willers, N
Keywords: Evidence mining
CRISP-DM
Cyber forensics
Knowledge discovery
Data mining
CRISP-EM
Digital investigation
Data mining process
Issue Date: Jan-2007
Publisher: SpringerLink.com
Citation: Venter, JP, De Waal, A and Willers, N. 2007. Specializing CRISP-DM for evidence mining. Advances in Digital Forensics III. IFIP International Federation for Information Processing, 2007, Volume 242/2007, pp 303-315
Abstract: Forensic analysis requires a keen detective mind, but the human mind has neither the ability nor the time to process the millions of bytes on a typical computer hard disk. Digital forensic investigators need powerful tools that can automate many of the analysis tasks that are currently being performed manually. This paper argues that forensic analysis can greatly benefit from research in knowledge discovery and data mining, which has developed powerful automated techniques for analyzing massive quantities of data to discern novel, potentially useful patterns. We use the term “evidence mining ” to refer to the application of these techniques in the analysis phase of digital forensic investigations. This paper presents a novel approach involving the specialization of CRISP-DM, a cross-industry standard process for data mining, to CRISP-EM, an evidence mining methodology designed specifically for digital forensics. In addition to supporting forensic analysis, the CRISP-EM methodology offers a structured approach for defining the research gaps in evidence mining.
Description: Advances in Digital Forensics III IFIP International Conference on Digital Forensics, National Centre for Forensic Science, Orlando, Florida, January 28-January 31, 2007
URI: https://springerlink3.metapress.com/content/tk122137q2263640/resource-secured/?target=fulltext.pdf&sid=oyp1qehvfgafffy1l1gzc0yk&sh=www.springerlink.com
http://hdl.handle.net/10204/5539
ISBN: 9780387737416
Appears in Collections:Information security
Digital intelligence
General science, engineering & technology

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