Name
Title | ||
|---|---|---|
Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results |
Abstract | ||
|---|---|---|
Current digital forensic text string search tools use match and/or indexing algorithms to search digital evidence at the physical level to locate specific text strings. They are designed to achieve 100% query recall (i.e. find all instances of the text strings). Given the nature of the data set, this leads to an extremely high incidence of hits that are not relevant to investigative objectives. Although Internet search engines suffer similarly, they employ ranking algorithms to present the search results in a more effective and efficient manner from the user's perspective. Current digital forensic text string search tools fail to group and/or order search hits in a manner that appreciably improves the investigator's ability to get to the relevant hits first (or at least more quickly). This research proposes and empirically tests the feasibility and utility of post-retrieval clustering of digital forensic text string search results - specifically by using Kohonen Self-Organizing Maps, a self-organizing neural network approach. This paper is presented as a work-in-progress. A working tool has been developed and experimentation has begun. Findings regarding the feasibility and utility of the proposed approach will be presented at DFRWS 2007, as well as suggestions for follow-on research. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.diin.2007.06.005 | Digital Investigation: The International Journal of Digital Forensics & Incident Response |
Keywords | DocType | Volume |
current digital forensic text,specific text string,thematically clustering search result,text string search,text string,efficient manner,string search tool,digital forensics,internet search engine,digital evidence,improving information retrieval effectiveness,order search hit,digital forensic text string,text clustering,digital forensics text string search text clustering self-organizing map kohonen,kohonen,self-organizing map,search result,search engine,neural network,information retrieval,work in progress,self organization,indexation,self organizing map | Journal | 4, |
Issue | ISSN | Citations |
Supplement | Digital Investigation | 39 |
PageRank | References | Authors |
2.18 | 27 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicole Lang Beebe | 1 | 177 | 18.22 |
| Jan Guynes Clark | 2 | 381 | 27.36 |