Name
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 re-search 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-0-387-73742-3_21 | ADVANCES IN DIGITAL FORENSIC III |
Keywords | Field | DocType |
data mining,evidence mining,CRISP-DM,CRISP-EM | Data science,Byte,World Wide Web,Digital forensics,Computer science,Knowledge extraction | Conference |
Volume | ISSN | Citations |
242 | 1571-5736 | 3 |
PageRank | References | Authors |
0.46 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacobus Venter | 1 | 9 | 1.61 |
| Alta de Waal | 2 | 42 | 5.68 |
| Cornelius Willers | 3 | 3 | 0.46 |