Detection of phishing attacks with machine learning techniques in cognitive security architecture

Ivan Ortiz Garces, Maria Fernada Cazares, Roberto Omar Andrade

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

21 Citas (Scopus)

Resumen

The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.

Idioma originalInglés
Título de la publicación alojadaProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas366-370
Número de páginas5
ISBN (versión digital)9781728155845
DOI
EstadoPublicada - dic. 2019
Evento6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 - Las Vegas, Estados Unidos
Duración: 5 dic. 20197 dic. 2019

Serie de la publicación

NombreProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019

Conferencia

Conferencia6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
País/TerritorioEstados Unidos
CiudadLas Vegas
Período5/12/197/12/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

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