Title
Towards Automatic Principles of Persuasion Detection Using Machine Learning Approach
Author
Bustio-Martínez, Lázaro (Universidad Iberoamericana)
Herrera-Semenets, Vitali (Centro de Aplicaciones de Tecnologías de Avanzada)
García-Mendoza, Juan-Luis (Université Sorbonne Paris Nord)
González-Ordiano, Jorge Ángel (Universidad Iberoamericana)
Zúñiga-Morales, Luis (Universidad Iberoamericana)
Sánchez Rivero, Rubén (Centro de Aplicaciones de Tecnologías de Avanzada)
Quiróz-Ibarra, José Emilio (Universidad Iberoamericana)
Santander-Molina, Pedro Antonio (Pontificia Universidad Católica de Valparaíso)
van den Berg, Jan (TU Delft Cyber Security)
Buscaldi, Davide (Université Sorbonne Paris Nord)
Contributor
Hernández Heredia, Yanio (editor)
Milián Núñez, Vladimir (editor)
Ruiz Shulcloper, José (editor)
Date
2024
Abstract
Persuasion is a human activity of influence. In marketing, persuasion can help customers find solutions to their problems, make informed choices, or convince someone to buy a useful (or useless) product or service. In computer crimes, persuasion can trick users into revealing sensitive information, or even performing actions that benefit attackers. Phishing is one of the most common and dangerous forms of persuasion-based attacks, as it exploits human vulnerabilities rather than technical ones. Therefore, an intelligent system capable of detecting and classifying persuasion attempts might be useful in protecting users. In this work, an approach that uses Machine Learning to analyze messages based on principles of persuasion and different data representations is presented. The aim of this research is to detect which data representation and which classification algorithm obtain the best results in detecting each principle of persuasion as a prior step to detecting phishing attacks. The results obtained indicate that among the combinations tested, there is one combination of data representation and classification algorithm that performs best. The related classification models obtained can detect the principles of persuasion at a rate that varies between 0.78 and 0.86 of AUC-ROC.
Subject
Principles of Persuasion
Machine Learning
Artificial Intelligence
Data representation
Phishing detection
To reference this document use:
http://resolver.tudelft.nl/uuid:c86fc708-a912-4105-a684-2b660ed119fe
DOI
https://doi.org/10.1007/978-3-031-49552-6_14
Publisher
Springer, Cham
Embargo date
2024-06-20
ISBN
978-3-031-49551-9
Source
Progress in Artificial Intelligence and Pattern Recognition - 8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023, Proceedings
Event
IWAIPR 2023: International Workshop on Artificial Intelligence and Pattern Recognition, 2023-09-27 → 2023-09-29, Varadero, Cuba
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 14335 LNCS
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2024 Lázaro Bustio-Martínez, Vitali Herrera-Semenets, Juan-Luis García-Mendoza, Jorge Ángel González-Ordiano, Luis Zúñiga-Morales, Rubén Sánchez Rivero, José Emilio Quiróz-Ibarra, Pedro Antonio Santander-Molina, Jan van den Berg, Davide Buscaldi