Hazard Grading Model of Terrorist Attack Based on Machine Learning

Abstract

In this paper, there is no unified grading standard for the harm of terrorist attacks. A classification model of terrorist incidents based on machine learning is proposed. First, the data related to the hazard in the Global Terrorism Database (GTD) is extracted and preprocessed. Secondly, the data is extracted by principal component analysis, and all events are aggregated into 5 by K-means clustering. Again, the entropy method is used to calculate the weighting coefficient of each indicator, and the comprehensive score of the hazard of each type of terrorist attack is calculated. Finally, the scores are divided into 1-5 levels of hazard grading models in order of high to low. The results show that the hazard grading model can scientifically and objectively quantify terrorist attacks.