Estimating Vulnerabilities in Large Covert Networks

Abstract

Covert organizations, such as terrorist groups, have network structures that are distinct from those in typical hierarchical organizations; e.g., they are cellular and distributed. Reasoning about how to attack dynamic networked organizations, let alone figuring out how they are likely to evolve, change, and adapt is terribly difficult. In this paper, an approach to estimating vulnerabilities and the impact of eliminating those vulnerabilities in covert networks is presented. Key features of this work include: using detailed network data to supplement high level views of organizations to create a composite image, using network metrics for multi-mode, multi-plex data to characterize key actors and the network itself, and using multi-agent simulation to predict change in the composite network view over time. Uncertainty is handled by using two types of data to reduce uncertainty, running the model in a Monte-Carlo fashion to determine the robustness of the results, and examining the robustness of the results under adding and dropping nodes and edges in the underlying networks. This approach is illustrated by contrasting the differential predictions for al-Qaida and Hamas as the top leaders are removed.