Crime Profiling System

Abstract

Digital forensics aims to examine a wide range of digital media in a “forensically” sound manner. This can be used either to uncover rationale for a committed crime and possible suspects, prevent a crime from taken place or to identify a threat so that it can be dealt with. The latter is firmly rooted within the domain of intelligence counter measures. The authors call the outcome of the analyses subject profiling where a subject can be a threat or a suspect. In this Chapter the authors outline a process for profiling based on Self-organizing Map (SOM) and evaluating our technique by profiling crimes using a multi-lingual corpus. The development and application of a Crime Profiling System (CPS) is also presented. The system is able to extract meaningful information (type of crime, location and nationality), from Arabic language crime news reports. The system has two unique attributes; firstly, information extraction depends on local grammar, and secondly, automatic generation of dictionaries. It is shown that the CPS improves the quality of the data through reduction where only meaningful information is retained. Moreover, when clustering, using Self Organizing Map (SOM), we gain efficiency as the data is cleansed by removing noise. The proposed system is validated through experiments using a corpus collated from different sources; Precision, Recall and F-measure are used to evaluate the performance of the proposed information extraction approach. Also, comparisons are conducted with other systems.