Proposal / Submission Type
Peer Reviewed Paper
Location
Las Vegas, Nevada
Abstract
One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on the data warehouse. We discuss several ways of utilizing the data warehouse using OLAP and data mining technologies. We finally discuss legal issues and data population issues for the data warehouse.
Scholarly Commons Citation
Song, Il-Yeol; Maguire, John D.; Lee, Ki Jung; Choi, Namyoun; Hu, Xiaohua; and Chen, Peter, "Designing a Data Warehouse for Cyber Crimes" (2016). Annual ADFSL Conference on Digital Forensics, Security and Law. 1.
https://commons.erau.edu/adfsl/2006/session-i/1
Included in
Computer Engineering Commons, Computer Law Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, Information Security Commons
Designing a Data Warehouse for Cyber Crimes
Las Vegas, Nevada
One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on the data warehouse. We discuss several ways of utilizing the data warehouse using OLAP and data mining technologies. We finally discuss legal issues and data population issues for the data warehouse.
