TerrorMine: Automatically Identifying the Group behind a Terrorist Attack

Alan Falzon, Joel Azzopardi

2022

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 group. 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).

Paper Citation

in Harvard Style

Falzon A. and Azzopardi J. (2022). TerrorMine: Automatically Identifying the Group behind a Terrorist Attack. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 221-228. DOI: 10.5220/0011540300003335

in Bibtex Style

@conference{kdir22,
author={Alan Falzon and Joel Azzopardi},
title={TerrorMine: Automatically Identifying the Group behind a Terrorist Attack},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={221-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011540300003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - TerrorMine: Automatically Identifying the Group behind a Terrorist Attack
SN - 978-989-758-614-9
AU - Falzon A.
AU - Azzopardi J.
PY - 2022
SP - 221
EP - 228
DO - 10.5220/0011540300003335
PB - SciTePress