DETECTOR: Automatic detection system for terrorist attack trajectories

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
EditoresHugo Alatrista-Salas, Juan Antonio Lossio-Ventura, Denisse Muñante
Lugar de publicaciónCham
EditorialSpringer
Páginas160-173
Número de páginas14
ISBN (versión digital)9783030116798
ISBN (versión impresa)9783030116798
DOI
EstadoPublicada - 1 ene. 2019
EventoCommunications in Computer and Information Science -
Duración: 1 ene. 2019 → …

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen898
ISSN (versión impresa)1865-0929

Conferencia

ConferenciaCommunications in Computer and Information Science
Período1/01/19 → …

Nota bibliográfica

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Palabras clave

  • Similarity
  • Terrorist
  • Trajectory

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