An Outlier-Based Intention Detection for Discovering Terrorist Strategies
| Autor: | Murat Akça, Mohammad T. Khasawneh, Salih Tutun, Ömer Bıyıklı |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science 02 engineering and technology Computer security computer.software_genre 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Terrorism Outlier 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing computer General Environmental Science |
| Zdroj: | Procedia Computer Science. 114:132-138 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2017.09.006 |
| Popis: | Terrorist groups (attackers) always strive to outmaneuver counter-terrorism agencies with different tactics and strategies for making successful attacks. Therefore, understanding unexpected attacks (outliers) is becoming more and more important. Studying such attacks will help identify the strategies from past events that will be most dangerous when counter-terrorism agencies are not ready for protection interventions. In this paper, we propose a new approach that defines terrorism outliers in the current location by using non-similarities among attacks to identify unexpected interactions. The approach is used to determine possible outliers in future attacks by analyzing the relationships among past events. In this approach, we calculate the relationship between selected features based on a proposed similarity measure that uses both categorical and numerical features of terrorism activities. Therefore, extracting relations are used to build the terrorism network for finding outliers. Experimental results showed that the comparison of actual events and the detected patterns match with more than 90% accuracy for many future strategies. Based on the properties of the outliers, counter-terrorism agencies can prevent a future bombing attack on strategic locations. (c) 2017 The Authors. Published by Elsevier B.V. |
| Databáze: | OpenAIRE |
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