TY - GEN
T1 - A computationally-enabled analysis of Lashkar-e-Taiba attacks in Jammu & Kashmir
AU - Mannes, A.
AU - Shakarian, J.
AU - Sliva, A.
AU - Subrahmanian, V. S.
PY - 2011
Y1 - 2011
N2 - Lashkar-e-Taiba (LeT for short) is one of the deadliest terrorist groups in the world.With over 100 attacks worldwide since 2004, LeT has become a political force within Pakistan, a proxy militia for the Pakistani Army, and a terror group that can carry out complex, coordinated attacks such as the 2008 Mumbai attacks. We have collected 25 years of data about LeT starting in 1985 and ending in 2010. The data is recorded on a monthly basis and includes the values of approximately 770 variables for each month. The variables fall into two categories-action variables describing actions taken by LeT during a given month and environmental variables describing the state of the environment in which LeT was functioning. Based on this data, we have used our Stochastic Opponent Modelling Agent (SOMA) platform to automatically learn models of LeT's behavior. These models describe conditions under which LeT took various actions- more importantly, the conditions act as predictors of when they will take similar actions in the future. In this paper, we focus on attacks by LeT in Jammu& Kashmir1. We describe some conditions under which LeT ramps up offensive activities in Jammu& Kashmir. We conclude with some policy options that may reduce the use of violence by LeT as indicated by the rules presented here.
AB - Lashkar-e-Taiba (LeT for short) is one of the deadliest terrorist groups in the world.With over 100 attacks worldwide since 2004, LeT has become a political force within Pakistan, a proxy militia for the Pakistani Army, and a terror group that can carry out complex, coordinated attacks such as the 2008 Mumbai attacks. We have collected 25 years of data about LeT starting in 1985 and ending in 2010. The data is recorded on a monthly basis and includes the values of approximately 770 variables for each month. The variables fall into two categories-action variables describing actions taken by LeT during a given month and environmental variables describing the state of the environment in which LeT was functioning. Based on this data, we have used our Stochastic Opponent Modelling Agent (SOMA) platform to automatically learn models of LeT's behavior. These models describe conditions under which LeT took various actions- more importantly, the conditions act as predictors of when they will take similar actions in the future. In this paper, we focus on attacks by LeT in Jammu& Kashmir1. We describe some conditions under which LeT ramps up offensive activities in Jammu& Kashmir. We conclude with some policy options that may reduce the use of violence by LeT as indicated by the rules presented here.
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U2 - 10.1109/EISIC.2011.61
DO - 10.1109/EISIC.2011.61
M3 - Conference contribution
AN - SCOPUS:81255128326
SN - 9780769544069
T3 - Proceedings - 2011 European Intelligence and Security Informatics Conference, EISIC 2011
SP - 224
EP - 229
BT - Proceedings - 2011 European Intelligence and Security Informatics Conference, EISIC 2011
T2 - 2011 1st European Intelligence and Security Informatics Conference, EISIC 2011
Y2 - 12 September 2011 through 14 September 2011
ER -
