Phishing Detection Using Deep Learning Attention Techniques

Safonov, Yehor

Phishing Detection Using Deep Learning Attention Techniques

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131_EEICT_2021_2.pdf(786.9 KB)

Date

2021

Authors

Safonov, Yehor

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

In the modern world, electronic communication is defined as the most used technologyfor exchanging messages between users. The growing popularity of emails brings about considerablesecurity risks and transforms them into an universal tool for spreading phishing content. Even thoughtraditional techniques achieve high accuracy during spam filtering, they do not often catch up to therapid growth and evolution of spam techniques. These approaches are affected by overfitting issues,may converge into a poor local minimum, are inefficient in high-dimensional data processing andhave long-term maintainability problems. The main contribution of this paper is to develop and trainadvanced deep networks which use attention mechanisms for efficient phishing filtering and text understanding.Key aspects of the study lie in a detailed comparison of attention based machine learningmethods, their specifics and accuracy during the application to the phishing problem. From a practicalpoint of view, the paper is focused on email data corpus preprocessing. Deep learning attention basedmodels, for instance the BERT and the XLNet, have been successfully implemented and comparedusing statistical metrics. Obtained results show indisputable advantages of deep attention techniquescompared to the common approaches.

Keywords

artificial intelligence, attention mechanism, deep learning, NLP, phishing filtering, textclassification, transformers

Citation

Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 131-135. ISBN 978-80-214-5943-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni

Document type

Peer-reviewed

Document version

Published version

Language of document

en

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