Identifying individuals associated with organized criminal networks: A social network analysis

The past couple of decades has witnessed an unprecedented rise in organized crime. This rise, coupled with increasing intricacies of organized crime, poses significant and evolving challenges for international law enforcement authorities. With the passage of time, authorities such as Interpol, have discovered that modern criminal organizations have adopted a networked structure, a shift away from the traditional hierarchical structure. Fluid networked structures make it difficult for the authorities to apprehend individuals associated with each network, and consequently, to disrupt the operations of the network. Various research groups have analyzed prison/courtroom transcripts, to create an organizational structure of known individuals, or a social network of individuals, suspected to be a part of a major drug/terrorist organization. These social networks have been studied fairly extensively from network centrality perspectives, to understand the role of suspect individuals in the network. Additionally, with drug and terror offenses increasing globally, the list of suspect individuals has also been growing over the past decade. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails higher resource requirements on the part of the authorities, and monitoring all the suspects soon becomes an impossible task. In this paper, we primarily focus on two types of networks – (i) Drug Trafficking Organizations, and (ii) Terrorist Organizations, and present a methodology for the surveillance of individuals associated with these networks. Our methodology is based on the mathematical notion of Identifying Codes, which ensures reduction in resources on the part of law enforcement authorities, without compromising the ability to uniquely identify a suspect, when they become “active” in drug/terror related activities. Furthermore, we show that our approach requires far lesser resources when compared to strategies adopting standard network centrality measures for the unique identification of individuals. In other words, we show that the strategy of monitoring individuals in such networks, by utilizing centrality measures is wasteful, on the part of the authorities. Finally, we evaluate the efficacy of our approach on real world datasets.