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Mining criminal networks from unstructured text documents


Mining criminal networks from unstructured text documents

Al-Zaidy, Rabeah and Fung, Benjamin C.M. and Youssef, Amr M. and Fortin, Francis (2012) Mining criminal networks from unstructured text documents. Digital Investigation, 8 (3-4). pp. 147-160. ISSN 17422876

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Official URL: http://dx.doi.org/10.1016/j.diin.2011.12.001


Digital data collected for forensics analysis often contain valuable information about the suspects’ social networks. However, most collected records are in the form of unstructured textual data, such as e-mails, chat messages, and text documents. An investigator often has to manually extract the useful information from the text and then enter the important pieces into a structured database for further investigation by using various criminal network analysis tools. Obviously, this information extraction process is tedious and error-prone. Moreover, the quality of the analysis varies by the experience and expertise of the investigator. In this paper, we propose a systematic method to discover criminal networks from a collection of text documents obtained from a suspect’s machine, extract useful information for investigation, and then visualize the suspect’s criminal network. Furthermore, we present a hypothesis generation approach to identify potential indirect relationships among the members in the identified networks. We evaluated the effectiveness and performance of the method on a real-life cybercrimine case and some other datasets. The proposed method, together with the implemented software tool, has received positive feedback from the digital forensics team of a law enforcement unit in Canada.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Authors:Al-Zaidy, Rabeah and Fung, Benjamin C.M. and Youssef, Amr M. and Fortin, Francis
Journal or Publication:Digital Investigation
ID Code:974920
Deposited On:30 Oct 2012 16:02
Last Modified:30 Oct 2012 16:02
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