TY - JOUR
T1 - A survey of quantitative models of terror group behavior and an analysis of strategic disclosure of behavioral models
AU - Serra, Edoardo
AU - Subrahmanian, V. S.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/1
Y1 - 2014/3/1
N2 - There are many applications (e.g., counter-terrorism) where we can automatically learn a quantitative model from realworld data about terror group behavior. In this paper, we first provide a survey of quantitative models of terrorist groups. To date, however, the best-known quantitative models of terror group behavior are based on various types of quantitative logic programs. After our survey, we address an important question posed to us by Nobel laureate, Tom Schelling. Once a set of quantitative logic behavior rules about an adversary has been learned, should these rules be disclosed or not? We develop a game theoretic framework in order to answer this question with a defender who has to decide what rules to release publicly and which ones to keep hidden. We first study the attacker's optimal attack strategy, given a set of disclosed rules, and then we study the problem of which rules to disclose so that the attacker's optimal strategy has minimal effectiveness. We study the complexity of both problems, present algorithms to solve both, and then present a (1-1/e )-approximation algorithm that (under some restrictions) uses a submodularity property to compute the optimal defender strategy. Finally, we provide experimental results showing that our framework works well in practice-these results are also shown to be statistically significant.
AB - There are many applications (e.g., counter-terrorism) where we can automatically learn a quantitative model from realworld data about terror group behavior. In this paper, we first provide a survey of quantitative models of terrorist groups. To date, however, the best-known quantitative models of terror group behavior are based on various types of quantitative logic programs. After our survey, we address an important question posed to us by Nobel laureate, Tom Schelling. Once a set of quantitative logic behavior rules about an adversary has been learned, should these rules be disclosed or not? We develop a game theoretic framework in order to answer this question with a defender who has to decide what rules to release publicly and which ones to keep hidden. We first study the attacker's optimal attack strategy, given a set of disclosed rules, and then we study the problem of which rules to disclose so that the attacker's optimal strategy has minimal effectiveness. We study the complexity of both problems, present algorithms to solve both, and then present a (1-1/e )-approximation algorithm that (under some restrictions) uses a submodularity property to compute the optimal defender strategy. Finally, we provide experimental results showing that our framework works well in practice-these results are also shown to be statistically significant.
KW - Behavior modeling
KW - counter-terrorism
KW - disclosure
KW - game theory
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=84907551587&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1109/TCSS.2014.2307454
U2 - 10.1109/TCSS.2014.2307454
DO - 10.1109/TCSS.2014.2307454
M3 - Review article
AN - SCOPUS:84907551587
VL - 1
SP - 66
EP - 88
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 1
M1 - 6804661
ER -