Learning future terrorist targets through temporal meta-graphs
| Autor: | Mihovil Bartulovic, Kathleen M. Carley, Gian Maria Campedelli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
computational modeling Computer Science - Machine Learning terrorism artificial intelligence forecasting jihadism Afghanistan Iraq computational modeling theories of terrorism Computer Science - Artificial Intelligence Computer science Feature vector Science 0211 other engineering and technologies forecasting Context (language use) 02 engineering and technology 01 natural sciences Article Machine Learning (cs.LG) 010104 statistics & probability Feature (machine learning) 0101 mathematics Dimension (data warehouse) 021110 strategic defence & security studies Multidisciplinary Event (computing) business.industry Deep learning Computational science Afghanistan terrorism artificial intelligence Data science Artificial Intelligence (cs.AI) Iraq Terrorism Medicine jihadism Artificial intelligence business Centrality theories of terrorism |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence. Comment: 19 pages, 18 figures |
| Databáze: | OpenAIRE |
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