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Mining criminal networks from unstructured text documents

Author:
Rabeah Al-Zaidya   Benjamin C.M. Funga   fung@ciise.concordia.ca   Amr M. Youssefa   Francis Fortinb  


Journal:
Digital Investigation


Issue Date:
2012


Abstract(summary):

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.


Page:
147-160


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