Efficient Mining of Criminal Networks from Unstructured Textual Documents
V.Vinodhini1 and M.Hemalatha2
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Digital data unruffled for forensics analysis often contain expensive information about the suspects’ social networks. However, most collected records are in the form of amorphous 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 monotonous 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 evaluate the usefulness and recital of the method on a real-life cybercriminal case and some other datasets.