The Pennsylvania State University The Graduate School The College of Information Sciences and Technology A COMPARATIVE SOCIAL NETWORK ANALYSIS OF THE 2008 MUMBAI, 2015 PARIS, and 2016 BRUSSELS TERRORIST NETWORKS A Thesis in Information Sciences and Technology by Tyler J. Yazujian 2017 Tyler J. Yazujian Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science May 2017 The thesis of Tyler J. Yazujian was reviewed and approved* by the following: Peter Forster Senior Lecturer of Information Sciences and Technology Thesis Adviser Jessica Kropczynski Lecturer of Information Sciences and Technology T { Donald Shemanski Professor of Practice of Information Sciences and Technology Andrea H. Tapia Associate Professor of Information Sciences and Technology Head of the Graduate Department in the College of IST *Signatures are on file in the Graduate School ii ABSTRACT This research builds a further understanding about analyses to characterize networks with limited data available. It uses social network analysis to retrospectively compare the networks of the terrorist attacks in Mumbai 2008, Paris November 2015, and Brussels March 2016, to better recognize the roles and positions of the networks’ actors. Expanding on previous analysis of the Mumbai terrorist network, this paper identifies new methods to study dark networks by applying social network analysis to the Mumbai, Paris, and Brussels networks. Three levels of analysis are conducted: (1) an attribute-level correlation to examine correlation between age and organizational role across cells; (2) key player analysis to investigate whether key players share similar roles; and (3) application of structural block models to the networks to identify cellular combat teams. iii TABLE OF CONTENTS List of Figures………………………………………………………………………………v List of Tables………………………………………………………………………………vi Acknowledgments………………………………………………………………………... vii Chapter 1 Introduction ............................................................................................................. 1 Chapter 2 Events Orientation ................................................................................................... 3 2.1 Mumbai, 2008 ............................................................................................................ 3 2.2 Paris, 2015 .................................................................................................................. 4 2.3 Brussels, 2016 ............................................................................................................ 5 Chapter 3 Literature Review .................................................................................................... 7 Chapter 4 Research Questions ................................................................................................. 16 Chapter 5 Methodology ........................................................................................................... 19 Chapter 6 Results ..................................................................................................................... 21 6.1 Attribute-Level Correlation Analysis (RQ1) ............................................................. 22 6.2 Key Player Analysis (RQ2) ........................................................................................ 24 6.2.1 Key Player Negative ........................................................................................ 24 6.2.2 Key Player Positive ......................................................................................... 28 6.3 Structural Blockmodeling (RQ3) ............................................................................... 30 Chapter 7 Discussion and Conclusion ..................................................................................... 34 References ................................................................................................................................ 37 iv LIST OF FIGURES Figure 1-1: Number of Terror attacks globally since 1990. Global Terrorism Database START (LaFree & Dugan, 2007) .................................................................................... 1 Figure 2-1: Target Locations in Mumbai (BBC News South Asia, 2008). .............................. 4 Figure 2-2: Target Locations in Paris (BBC News Europe, 2015). ......................................... 5 Figure 2-3: Target Locations in Brussels (Wagner, 2016). ...................................................... 7 Figure 3-1: Number of Lone Wolf Attackers since 1950s (Worth, 2016) ............................... 11 Figure 3-2: ISIS Attacks, Outside of its Self-Proclaimed Caliphate (Callimachi, 2017) ......... 11 Figure 3-3: Network with Central Node “1” that Does Not Fragment the Network (Borgatti, 2006) ................................................................................................................ 13 Figure 6-1: A Depiction of the Mumbai Terrorist Network (Borgatti et al., 2002) ................. 21 Figure 6-2: A Depiction of the Paris Terrorist Network (Borgatti et al., 2002) ...................... 22 Figure 6-3: A Depiction of the Brussels Terrorist Network (Borgatti et al., 2002) ................. 22 v LIST OF TABLES Table 6-1: Results for removal of one key player, based on fragmentation (Borgatti, S. P., 2003). ............................................................................................................................... 26 Table 6-2: Results for removal of two key players, based on fragmentation (Borgatti, S. P., 2003). .......................................................................................................................... 27 Table 6-3: Results for monitoring of one key player, based on reach (Borgatti, S. P., 2003). ............................................................................................................................... 29 Table 6-4: Results for monitoring two key players, based on reach (Borgatti, S. P., 2003). ... 30 Table 6-5: Block model output for the Mumbai network. Handler (H); Attacker (A). ........... 31 Table 6-6: Block Model Output for Paris Network. Handler (H); Attacker (A). ..................... 32 Table 6-7: Block Model Output for Brussels Network. Handler (H); Operational Support (O); Attacker (A). ............................................................................................................. 33 vi ACKNOWLEDGEMENTS I’d like to give thanks to the following people who have greatly helped me along the way. This thesis is dedicated to the following people: My parents: You both have always loved and supported me in anything I pursued. Dr. Pete Forster: You have been an exemplary advisor to my work and to my studies. You have always had an open door to your students and it was a great pleasure working with you. Dr. Don Shemanski: Your classes sparked my interest in counterterrorism efforts and national security. Each class provided students with an incredible learning environment with hands on scenarios that I still and will talk about throughout the rest of my life. Dr. Jess Kropczynski: Your classes sparked my interest in social network analysis and their applications to terrorist organizations. Your willingness to always help your students in anything they needed did not go unnoticed. Col. Jake Graham: Your classes, research, and providing of the Red Cell Lab have all been extremely valuable to my studies. You were a mentor to me who always went out of your way to help me in anything I was pursuing. Alex Brown: Your help during the IST 597 course as the TA and providing me with the Mumbai dataset set the foundation for this work. The Penn State Football Coaches and Academic Staff: You each played a vital role in my athletic and academic success on and off the field. Specifically, I’d like to thank Coach James Franklin, Coach Charles Huff, Coach Sam Williams, Todd Kulka, Molly Tye, and Coach Bill O’Brien vii 1 Chapter 1 Introduction Gaining and in-depth understanding of how terrorist organizations and their respective cells are structured and operate is a high priority for national security entities to combat terrorism in the post-9/11 world. According to the Global Terrorism Database, the number of terror attacks worldwide has increased dramatically since 1990 (see Figure 1-1) (LaFree & Dugan, 2007). Since 9/11, the U.S. has spent $1.6 trillion on the counterterrorism efforts in hopes to halt the upward trend (Belasco, 2009). Terror attacks have increased despite spending, which has raised the call for academic and practitioners alike to improve methods to understand terrorism and the social infrastructure that allows them to thrive. This paper works to advance this area by exploring social network analysis (SNA) techniques not commonly used to explore dark networks. It presents a comparative analysis of the social infrastructure involving in three different terror networks through three methodological techniques and describes their contribution to understanding networks. Such an understanding may help future initiatives to disrupt activities before violence occurs. Figure 1-1: Number of Terror attacks globally since 1990. Global Terrorism Database START (LaFree & Dugan, 2007) 2 SNA is a methodology that allows researchers to quantify and visualize social infrastructure, which creates unique opportunities to begin untangling the complexity that underlies terrorism. Although previous work has opened the door to the application of SNA to terrorist networks, Ressler (2006) contends that