Clasificación de capturas de smishing con aprendizaje profundo e IRIS

  1. Blanco Medina, Pablo 1
  2. Carofilis, Andrés 1
  3. Fidalgo, Eduardo 1
  4. Alegre, Enrique 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Journal:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Year of publication: 2024

Issue: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10904 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

Smishing, a variant of phishing that uses the Short Message Service, uses smartphones and the trust of people in text messaging as a communication tool to spread more easily. When citizens report these suspicious messages to Computer Emergency Response Teams, they usually do it through a screenshot of their smartphone. Response Teams may find useful an automatic tool that classifies Smishing into different categories before proceeding to further information extraction. We propose to compare the performance of customized Convolutional Neural Networks and Vision Transformers with their pre-trained versions on ImageNet datasets for automatically classifying smishing screenshots into two different categories: joint and separate text. We make publicly available a novel dataset, IRIS-244, containing 244 smishing screenshots with phishing URLs. Combined with data augmentation techniques, we discovered that Xception architecture out performs the rest of the approaches, with an accuracy score of 78,36.

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