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Biblioteca Digital Minerva
Bachelor thesis . 2025
License: CC BY NC ND
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Trucotrato: Sistema de detección de estafas digitales

Authors: Martínez Venegas, Cristhian Felipe;

Trucotrato: Sistema de detección de estafas digitales

Abstract

TrucoTratoTechcamp es una solución tecnológica diseñada para ayudar a los usuarios a identificar posibles estafas digitales mediante la validación de números telefónicos, análisis de imágenes y evaluación de señales de riesgo. El sistema integra un frontend moderno en React, un backend en Node.js con TypeScript y una base de datos que centraliza registros y listas negras. Además, emplea Google Vision API para el análisis de imágenes y Vertex AI para la detección avanzada de patrones de fraude. El documento presenta las etapas del proyecto, la arquitectura del sistema, el diseño, el plan de pruebas y el proceso que permite validar la aceptación del usuario. Finalmente, describe cómo la aplicación resuelve el problema de estafas virtuales mediante un diseño escalable, seguro y orientado a una experiencia de usuario confiable.

Resumen Introducción Objetivos 3.1 Objetivo General 3.2 Objetivos Específicos Definición del Problema 4.1 Planteamiento 4.2 Problema Central Pregunta de Investigación 5.1 Subpreguntas Ámbito y Delimitación Aspectos Éticos y Legales Justificación 8.1 Impacto Social 8.2 Impacto Tecnológico 8.3 Innovación 8.4 Relevancia Académica Marco Teórico Diseño Metodológico 10.1 Tipo de Investigación 10.2 Método y Técnicas 10.3 Fases del Desarrollo 10.4 Instrumentos 10.5 Resultados Esperados Arquitectura y Diseño Técnico del Sistema Análisis de Costos 12.1 Costos Directos 12.2 Costos Indirectos 12.3 Capital de Trabajo 12.4 Total del Proyecto Evaluación Económica y Sostenibilidad 13.1 Sostenibilidad Ambiental 13.2 Sostenibilidad Económica 13.3 Sostenibilidad Social Conclusiones Recomendaciones Referencias Anexos ✅ 2. Texto re

TrucoTratoTechcamp is a technological solution designed to help users identify potential digital scams through phone number validation, image analysis, and the evaluation of risk indicators. The system integrates a modern React frontend, a Node.js/TypeScript backend, and a database for user management and blacklist storage. It also uses Google Vision API for image analysis and Vertex AI for advanced fraud detection. This document describes the project stages, system architecture, design approach, testing plan, and the process for validating user acceptance. Finally, it explains how the application solves the problem of virtual scams through a scalable, secure, and user-focused solution that ensures reliability.

Ingeniero de Sistemas

Pregrado

Country
Colombia
Related Organizations
Keywords

Fraude digital, Desarrollo de software, Protección de datos, Innovaciones tecnológicas, Digital fraud, Arquitectura de software, Seguridad informática

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green