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DETECTOR: Automatic Detection System for Terrorist Attack Trajectories

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Information Management and Big Data (SIMBig 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 898))

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Abstract

To guarantee national security against terrorist attacks or organized crime, states must implement homeland security solutions based on ubiquitous systems to know in advance the number of suspects involved in an attack. This work proposes a method, which combines popular trajectory similarity metrics to estimate the number of attackers participating in a malicious act through the analysis of the trajectories described by the attacker’s cell phone connection to antennas (i.e. Call Detail Records). Therefore, measuring trajectory similarity in CDRs generates different challenges compared to those similar metrics applied over GPS and video datasets.

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Notes

  1. 1.

    DETECTOR Website: github.com/bitmapup/detector.

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Authors and Affiliations

  1. Universidad del Pacífico, Av. Salaverry 2020, Lima, Peru

    Isaias Hoyos, Bruno Esposito & Miguel Nunez-del-Prado

Authors
  1. Isaias Hoyos
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  2. Bruno Esposito
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  3. Miguel Nunez-del-Prado
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Corresponding author

Correspondence to Miguel Nunez-del-Prado .

Editor information

Editors and Affiliations

  1. Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Juan Antonio Lossio-Ventura

  2. Fondazione Bruno Kessler, Trento, Italy

    Denisse Muñante

  3. Facultad de Ingeniería, University of the Pacific, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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Hoyos, I., Esposito, B., Nunez-del-Prado, M. (2019). DETECTOR: Automatic Detection System for Terrorist Attack Trajectories. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-11680-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11679-8

  • Online ISBN: 978-3-030-11680-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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