Tracking Changes in Resilience and Level of Coordination in Terrorist Networks

Abstract

The focus of this paper is on the activity profiles of certain networks, such as terrorist networks, that show frequent spurts and downfalls. Of particular interest is the quick detection of changes in two specific activity patterns corresponding to resilience and level of coordination in the network. Understanding changes in resilience and coordination could provide insights into the underlying organizational dynamics and aid in more informed decision-making. Prior work in tackling this problem is based on parametric approaches and relies on models developed with time-series analysis techniques, self-exciting hurdle models, or hidden Markov models. While these approaches detect spurts and downfalls reasonably accurately, they are all based on model learning—a task that is computationally difficult in practice because of the “rare” nature of terrorist attacks from a model learning perspective. In this paper, we pursue an alternate statistical nonparametric approach for spurt detection in activity profiles. Our approach is based on binning the count data of activity to form observation vectors that can be compared with each other. Motivated by a majorization theory framework, these vectors are then transformed via certain functionals and used in spurt detection and classification. While the parametric approaches often result in either a large number of missed detections of real changes or false alarms, the proposed approach is shown to result in a small number of missed detections and false alarms. Furthermore, since spurt detection is a problem of importance across multiple applications, the nonparametric nature of the approach makes it attractive in these applications.