https://orcid.org/0000-0001-6066-5336
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ABSTRACT
Terrorist attacks carried out by individuals have significantly accelerated over the last twenty years. This type of lone-actor (LA) terrorism stands as one of the greatest security threats of our time. While the research on LA behavior and characteristics has produced valuable information on demographics, classifications, and warning signs, the relationship among these characteristics is yet to be addressed. Moreover, the means of radicalization and attacking have changed over decades. This study conducts an a-posteriori analysis of the temporal changes in LA terrorism and behavioral associations in LAs. We initially identify twenty-five binary behavioral characteristics of LAs and analyze 190 LAs. Next, we classify LAs according to ideology first, incident-scene behavior (determined via a virtual attacker-defender game) secondly, and, finally, the clusters obtained from the data. In addition, within each class, statistically significant associations and temporal relations are extracted using the A-priori algorithm. These associations would be instrumental in identifying the attacker’s type and intervening at the right time. The results indicate that while pre-9/11 LAs were mostly radicalized by the people in their environment, post-9/11 LAs are more diverse. Furthermore, association chains for different LA types present unique characteristic pathways to violence and after-attack behavior.
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
Notes
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Additional information
Funding
Notes on contributors
Ayca Altay
Ayca Altay is a PhD student at the Industrial & Systems Engineering Department. She received her BS, MS, and previous PhD degrees in Industrial Engineering from Istanbul Technical University, Istanbul, Turkey. Her doctoral dissertation focuses on game-theoretical models for lone-actor terrorism.
Melike Baykal-Gürsoy
Melike Baykal-Gürsoy is a Professor and the Director of Laboratory for Stochastic Systems, and of GRIST-Game Research for Infrastructure SecuriTy Lab, in the department of Industrial and Systems Engineering at Rutgers University. She received her doctorate in Systems Engineering from the University of Pennsylvania, Philadelphia. Her specific fields of interest include stochastic modeling, queueing, Markov decision processes, stochastic games, and their applications. The current research in the Laboratory for Stochastic Systems focuses on the areas of modeling, optimization and control of stochastic systems, such as transportation, communication, and production/inventory networks. In GRIST Lab, Dr. Baykal-Gürsoy and her team are developing game-theoretic models and algorithms in order to protect infrastructure networks and their users against intelligent adversaries. Her research and teaching have been supported through grants from NSF, United Nations, DOD, and Transportation Coordinating Council/Federal Transit Administration. Dr. Baykal-Gürsoy is the co-author of a book entitled An Introduction to Probability and Statistics.
Pernille Hemmer
Pernille Hemmer is an Associate Professor at Department of Psychology. She received her PhD from the Department of Cognitive Science at the University of California, Irvine. She completed a post-doctoral fellowship in the Department of Psychology at Syracuse University before joining the faculty at Rutgers University in 2012. Her research seeks to elucidate the relationship between mental representations and naturalistic environments. While her work has been focused on episodic and semantic memory, the overarching theme of her research is decision making in naturalistic environments. She uses ecologically valid stimuli to capitalize on the idea that humans work in concert with their environment and that people use their knowledge and expectations to make decisions in a broad range of cognitive tasks. Specifically, she focuses on complex environments in which people make real world decisions about situations where knowledge of the environment can be brought to bear. In these environments, she applies computational and Bayesian modeling to behavioral experiments. She explores how people use information from these environments in retrieving information from memory, and decision making in general. Her research has been recognized with a best-paper award for modeling of higher-level cognition, as well as an NSF CAREER grant.