An Outlier-Based Intention Detection for Discovering Terrorist Strategies

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