HIERARCHY EXTRACTION FOR COVERT NETWORK DESTABILIZATION AND COUNTERTERRORISM MECHANISM

Abstract

Reduction of incessant crimes is the utmost priority of security agencies. Use of intelligence has potential to bring crime rate under minimal control but, organizational structure imbibes by criminal groups has been keeping and protecting covert members. However, extraction of covert members from flat organization structure has some challenges especially in identifying and analyzing high ranking criminals. Therefore, this paper proposed the used of eigenvector centrality for extraction of high rank members that social network analysis considered passive. Furthermore, this approach could offer a robust platform to detect clandestine nodes that attempt to escape detection.