Turkish Email Phishing Detection Using Fine Tuned Large Language Models

Egemen Gülserliler*, Deniz Egemen Keneş, Şerif Bahtiyar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Phishing emails are one of the most common types of attack used by adversaries worldwide. The adversaries prepare targeted phishing emails for specific users who generally use their native languages. On the other hand, phishing email detection mechanisms designed by global vendors are generally capable to detect phishing emails written in English. This circumstance is a big challenge for organizations that require detecting phishing emails in native languages, such as in Turkish. In this research, we propose a novel fine-tuned Large Language Model to detect Turkish phishing emails. We experimentally evaluated the proposed model with real phishing emails in Turkish. Analysis results show that the proposed model detects phishing emails in Turkish with high accuracy, which is not observed with global vendors phishing detection mechanisms for Turkish emails. Thus, native language based phishing detection mechanisms may provide better detections for phishing emails for native languages, such as Turkish.

Original languageEnglish
Title of host publication2025 15th International Conference on Advanced Computer Information Technologies, ACIT 2025 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages487-490
Number of pages4
ISBN (Electronic)9798331595432
DOIs
Publication statusPublished - 2025
Event15th International Conference on Advanced Computer Information Technologies, ACIT 2025 - Hybrid, Sibenik, Croatia
Duration: 17 Sept 202519 Sept 2025

Publication series

NameProceedings - International Conference on Advanced Computer Information Technologies, ACIT
ISSN (Print)2770-5218
ISSN (Electronic)2770-5226

Conference

Conference15th International Conference on Advanced Computer Information Technologies, ACIT 2025
Country/TerritoryCroatia
CityHybrid, Sibenik
Period17/09/2519/09/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

  • Fine Tuning
  • Large Language Models
  • Native Language
  • Phishing Email
  • Transfer Learning

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