Authors:
Alan Falzon
and
Joel Azzopardi
Affiliation:
Department of Artificial Intelligence, Faculty of ICT, University of Malta, Msida, Malta
Keyword(s):
Terrorism, Data-mining, GTD, Machine Learning, Prediction, Clustering, Forecasting.
Abstract:
Terrorism is a problem that provokes fear and causes death internationally. The Global Terrorism Database (GTD) contains a large number of terrorist attack records which can be used for data mining to help counter or mitigate future terror attacks. TerrorMine employs AI techniques to identify perpetrators responsible for terrorist attacks. Moreover, the effect of clustering beforehand is investigated, while also attempting to identify new (unknown) terrorist organisations, and predicting future activity of terror groups. Several experiments are performed. The Random Forest model obtains the highest Weighted F1-score when identifying responsible perpetrators. Furthermore, upon clustering the data using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBScan) before classification, training time is reduced by more than 50%. Various techniques are used for the unsupervised identification of whether a terrorist attack was carried out by an unknown terrorist grou
p. Nearest Neighbours gives the highest Macro F1-score when cross-validated. When forecasting the future impact of the different terrorist groups, Prophet achieved an F1-score higher than that of Autoregressive Integrated Moving Average (ARIMA).
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