Venter, JPDe Waal, AWillers, N2012-01-272012-01-272007-01Venter, 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 303-3159780387737416https://springerlink3.metapress.com/content/tk122137q2263640/resource-secured/?target=fulltext.pdf&sid=oyp1qehvfgafffy1l1gzc0yk&sh=www.springerlink.comhttp://hdl.handle.net/10204/5539Advances in Digital Forensics III IFIP International Conference on Digital Forensics, National Centre for Forensic Science, Orlando, Florida, January 28-January 31, 2007Forensic 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.enEvidence miningCRISP-DMCyber forensicsKnowledge discoveryData miningCRISP-EMDigital investigationData mining processBook ChapterVenter, J., De Waal, A., & Willers, N. (2007). Specializing CRISP-DM for evidence mining., <i></i> SpringerLink.com. http://hdl.handle.net/10204/5539Venter, JP, A De Waal, and N Willers. "Specializing CRISP-DM for evidence mining" In <i></i>, n.p.: SpringerLink.com. 2007. http://hdl.handle.net/10204/5539.Venter J, De Waal A, Willers N. Specializing CRISP-DM for evidence mining. [place unknown]: SpringerLink.com; 2007. [cited yyyy month dd]. http://hdl.handle.net/10204/5539.TY - Book Chapter AU - Venter, JP AU - De Waal, A AU - Willers, N AB - 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. DA - 2007-01 DB - ResearchSpace DP - CSIR KW - Evidence mining KW - CRISP-DM KW - Cyber forensics KW - Knowledge discovery KW - Data mining KW - CRISP-EM KW - Digital investigation KW - Data mining process LK - https://researchspace.csir.co.za PY - 2007 SM - 9780387737416 T1 - Specializing CRISP-DM for evidence mining TI - Specializing CRISP-DM for evidence mining UR - http://hdl.handle.net/10204/5539 ER -