Terrorism is a complex phenomenon with high uncertainties in user strategy. The uncertain nature of terrorism is a main challenge in the design of counter-terrorism policy. Government agencies (e.g., CIA, FBI, NSA, etc.) cannot always use social media and telecommunications to capture the intentions of terrorists because terrorists are very careful in the use of these environments to plan and prepare attacks. To address this issue, this research aims to propose a new framework by defining the useful patterns of suicide attacks to analyze the terrorist activity patterns and relations, to understand behaviors and their future moves, and finally to prevent potential terrorist attacks. In the framework, a new network model is formed, and the structure of the relations is analyzed to infer knowledge about terrorist attacks. More specifically, an Evolutionary Simulating Annealing Lasso Logistic Regression (ESALLOR) model is proposed to select key features for similarity function. Subsequently, a new weighted heterogeneous similarity function is proposed to estimate the relationships among attacks. Moreover, a graph-based outbreak detection is proposed to define hazardous places for the outbreak of violence. Experimental results demonstrate the effectiveness of our framework with high accuracy (more than 90% accuracy) for finding patterns when compared with that of actual terrorism events in 2014 and 2015. In conclusion, by using this intelligent framework, governments can understand automatically how terrorism will impact future events, and governments can control terrorists’ behaviors and tactics to reduce the risk of future events.
Tutun, Salih, Mohammad T. Khasawneh, and Jun Zhuang. 2017. "New Framework That Uses Patterns and Relations to Understand Terrorist Behaviors." Expert Systems with Applications (February). http://www.sciencedirect.com/science/article/pii/S0957417417301161