A network-based approach for understanding suicide attack behavior

Terrorists are increasingly using suicide attacks to attack different targets. The government finds it challenging to track these attacks since the terrorists have learned from experience to avoid unsecured communications such as social media. Therefore, we propose a new approach that will predict the characteristics of future suicide attacks by analyzing the relationship between past attacks. The proposed approach first identifies relevant features using a graph-based feature selection (GBFS) method, then calculates the relationship between selected features via a new similarity measure capable of handling both categorical and numerical features. The proposed approach was tested using a second terrorism data set; we were able to successfully predict the characteristics of this new testing data set using patterns extracted from the original data set. The results could potentially enable law enforcement agencies to propose reactive strategies.