As technology evolves, abuse and cybercrime evolve with it. Cybercriminals today abuse and monetize every aspect of technology. However, understanding how criminals make profit from online abuse and the effective ways of disrupting criminal efforts is still ad hoc, often based off of anecdotal evidence, specific to a particular cybercrime and accomplished primarily through analysis of limited structured metadata and painstaking manual analysis. The key challenge is to automate this process, since this labor intensive manual approach does not scale. The researcher proposes to build and evaluate a generalizable and scalable framework for automatically analyzing online crime. The framework will examine cybercrime as a community-based activity, analyze how information flows between different communities of cybercriminal networks, automatically discover the role of the communities and identify the cost-effective method for disrupting these networks.
Grant / January 2020