Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

The law enforcement agencies need up-to-the minute intelligence about terrorist threats, which makes the development of expanded and improved intelligence a pre-requisite for their success As such, the intelligence community is actively developing new technologies to facilitate the identification and targeting of new and emerging threats. These threats can be manifested in network-centric form of organizations, doctrines, and technologies attuned to the information age. It is a widely held belief that terrorist activities are done by dispersed organizations (like non-hierarchical organizations), small groups, and individuals who communicate, coordinate and conduct their campaign in a network-like manner. There is a pressing need to automatically collect data on terrorist networks, analyze such networks to find hidden relations and groups, prune datasets to locate regions of interest, detect key players, characterize the structure, locate points of vulnerability, and find the efficiency of the network. To meet this challenge, we designed and developed a knowledge-base for storing and manipulating data collected from various authenticated websites. This paper applies several network centrality measures (and combinations of them) to identify key players (important nodes) in terrorist networks. Our recently introduced techniques and algorithms (which are also implemented in the investigative data mining toolkit known as iMiner) will be particularly useful for law enforcement agencies that need to analyze terrorist networks and prioritize their targets. Applying recently introduced mathematical methods for constructing the hidden hierarchy of "nonhierarchical" terrorist networks; we present case studies of the terrorist attacks occurred / planned in the past, in order to identify hidden hierarchy of the networks involved in those tragic events.