FCTNLP: An architecture to fight cyberterrorism with natural language processing

Andrés Zapata Rozo, D. Díaz-López, J. Pastor-Galindo, Félix Gómez Mármol

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

53 Descargas (Pure)

Resumen

Law Enforcement Agencies (LEA) are everyday more and more concerned about illicit activities that may be found in cyberspace like cybercrimes, cyber espionage, cyberterrorism, cyber warfare, among others. In a cyberterrorism context, Hostile Social Manipulation (HSM) is a strategy that employs different manipulation methods mostly through social media to produce damage to a target state. The efforts to fight cyberterrorism could come along with new technologies that
allow a faster and more effective control of offensive actions. For that reason, this paper proposes an artificial intelligence-based solution that processes posts in social networks using Natural Language Processing (NLP) techniques, applying the following three models: i) Sentiment Model to discriminate between threat
and non-threat publications, ii) Similarity Model to identify suspects with similar intentions and iii) NER model that identifies entities in the text. Finally, the proposal was tested exhaustively to validate its functionality and feasibility, achieving an integrated and simple prototype.
Idioma originalInglés estadounidense
Título de la publicación alojadaVII Jornadas Nacionales de Investigación en Ciberseguridad (JNIC)
Lugar de publicaciónBilbao, Spain
EditorialFundación Tecnalia Research and Innovation
Páginas42-49
Número de páginas8
Volumen01
ISBN (versión digital)978-84-88734-13-6
EstadoPublicada - jun. 27 2022

Áreas temáticas de ASJC Scopus

  • Matemática computacional

Huella

Profundice en los temas de investigación de 'FCTNLP: An architecture to fight cyberterrorism with natural language processing'. En conjunto forman una huella única.

Citar esto