Preprocesado de imagen y OCR para mejorar deteccion de smishing

  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

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

ISSN: 3045-4093

Année de publication: 2024

Número: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10955 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

Résumé

The globalization of communication technologies has led to an increase in the number of scams through phishing. Computer Emergency Response Teams receive screenshots of smartphones from citizens containing short messages with suspicious messages. These SMS try to impersonate well-known companies and persuade users to take urgent action through a URL to steal their data or make unauthorized charges to their bank account. These short messages are called Smishing, and CERTs could be interested in tools that can automatically extract the URLs from these screenshots to verify later if it is a phishing URL. In this work, we propose a pipeline for Smishing URL extraction from the screenshots that CERTs may receive. We have combined traditional computer vision techniques, such as preprocessing or morphological operations, with an OCR to recognize the suspicious URLs. We have used our pipeline to 117 screenshots of Smishing messages containing 121 URLs, achieving an accuracy of 61,16 % retrieving complete URLs from Smishing screenshots.

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La source de données: Dialnet