Suspects Prediction towards Terrorist Attacks Based on Machine Learning

Terrorism is a common threat to mankind, and fighting terrorism is the responsibility of every country. Aiming at suspects prediction of terrorist attacks, five methods based on machine learning are used to sort the recorded data of terrorist attacks quantitatively. Based on Logical Regression, Decision Tree, Gaussian Bayesian Network, AdaBoost and Random Forest fitting model, the terrorist attacks recorded, Global Terrorism Database(GTD), in 2015 and 2016 were used as experimental data. The most likely suspects for each attack and the possibility of these attacks, which may be launched by the five most harmful terrorist organizations, will be sorted out and the ranking of these attacks launched by each terrorist organization in the incident will be shown out. The results show that Decision Tree has the highest accuracy and the precision among the candidates, and Gaussian Bayesian Network is close behind.