A big data driven multilevel deep learning framework for predicting terrorist attacks - Ume Kalsooma; Sahar Arshad; Amerah Albarah; Imran Siddiqi; Saeed Ullah; Abdul Mateen; Farhan Amin (Scientific Reports)
Abstract In recent years, terrorism has increasingly threatened human security, causing violence, fear, and damage to both the general public and specific targets. These attacks create unrest among individuals and within society. Leveraging the recent advancements in deep machine learning, several intelligent systems have been developed to predict terrorist attacks. However, existing state-of-the-art models are limited, lack support for big data, suffer from accuracy issues, and require extensive modifications. Therefore, to fill this gap, herein, we propose an integrated Big Data deep learning-based predictive model to predict the probability of a terrorist attack. We treat the series of terrorist activities as a sequence modeling problem and propose a big data long short-term memory network. It is a layered model capable of processing large-scale data. Our proposed model can learn from past events and forecast future attacks. The proposed model predicts the likely location of future attacks at the city, country, and regional levels. The experimental study of the proposed model was carried out on the samples in the global terrorism dataset, and promising results are reported on a number of standard evaluation metrics, accuracy, precision, Recall, and F1 score. The obtained results suggest that the proposed model contributes substantially to predicting the probability of an attack at a particular location. The identification of potential locations of an attack allows law enforcement agencies to take suitable preventative measures to combat terrorism effectively.