An Outlier-Based Intention Detection for Discovering Terrorist Strategies

摘要: Abstract Terrorist groups (attackers) always strive to outmaneuver counter-terrorism agencies with different tactics and strategies for making successful attacks. Therefore, understanding unexpected attacks (outliers) is becoming more important. Studying such will help identify the from past events that be most dangerous when are not ready protection interventions. In this paper, we propose a new approach defines terrorism outliers in current location by using non-similarities among interactions. The used determine possible future analyzing relationships events. approach, calculate relationship between selected features based on proposed similarity measure uses both categorical numerical of activities. extracting relations build network finding outliers. Experimental results showed comparison actual detected patterns match than 90% accuracy many strategies. Based properties outliers, can prevent bombing attack strategic locations.