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Efficient Mining of Criminal Networks from Unstructured Textual Documents

V.Vinodhini, M.Hemalatha

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.