Interoperability and Targeted Attacks on Terrorist Organizations Using Intelligent Tools From Network Science

Abstract

The early intervention of law enforcement authorities to prevent an impending terrorist attack is of utmost importance to ensuring economic, financial, and social stability. From our previously published research, the key individuals who play a vital role in terrorist organizations can be timely revealed. The problem now is to identify which attack strategy (node removal) is the most damaging to terrorist networks, making them fragmented and therefore, unable to operate under real-world conditions. We examine several attack strategies on 4 real terrorist networks. Each node removal strategy is based on: (i) randomness (random node removal), (ii) high strength centrality, (iii) high betweenness centrality, (iv) high clustering coefficient centrality, (v) high recalculated strength centrality, (vi) high recalculated betweenness centrality, (vii) high recalculated clustering coefficient centrality. The damage of each attack strategy is evaluated in terms of Interoperability, which is defined based on the size of the giant component. We also examine a greedy algorithm, which removes the node corresponding to the maximal decrease of Interoperability at each step. Our analysis revealed that removing nodes based on high recalculated betweenness centrality is the most harmful. In this way, the Interoperability of the communication network drops dramatically, even if only two nodes are removed. This valuable insight can help law enforcement authorities in developing more effective intervention strategies for the early prevention of impending terrorist attacks. Results were obtained based on real data on social ties between terrorists (physical face-to-face social interactions).