Feasibility Study of Social Network Analysis on Loosely Structured Communication Networks

Abstract

Organised criminal groups are moving more of their activities from traditionally physical crime into the cyber domain; where they form online communities that are used as marketplaces for illegal materials, products and services. The trading of illicit goods drives an underground economy by providing services that facilitate almost any type of cyber crime. The challenge for law enforcement agencies is to know which individuals to focus their efforts on, in order to effectively disrupting the services provided by cyber criminals. This paper present our study to assess graph-based centrality measures’ performance for identifying important individuals within a criminal network. These measures has previously been used on small and structured general social networks. In this study, we are testing the measures on a new dataset that is larger, loosely structured and resembles a network within cyber criminal forums. Our result shows that well established measures have weaknesses when applied to this challenging dataset.