Detecting Turkish Phishing Attack with Machine Learning Algorithm

Melih Turhanlar, Cengiz Acartürk, Cengiz Acartürk

2021

Abstract

Phishing attacks are social engineering attacks that aim at stealing a victim’s personal information. The primary motivation is the exploitation of human emotion. The body of a phishing message usually includes a webpage link, aiming at convincing the victim to click and submit credentials. The victim typically connects to a mock webpage. There exist solutions for mitigating phishing attacks, such as phishing detection by Natural Language Processing (NLP). We present a framework for detecting phishing text in Turkish by running machine learning classifiers on an imbalanced phishing data set. The training dataset includes emails, SMS, and tweets. Our findings reveal that the Logistic Regression Synthetic Minority Over-Sampling Technique achieves high performance compared to a set of machine learning models tested in the study.

Paper Citation

in Harvard Style

Turhanlar M. and Acartürk C. (2021). Detecting Turkish Phishing Attack with Machine Learning Algorithm. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, ISBN 978-989-758-536-4, pages 577-584. DOI: 10.5220/0010717900003058

in Bibtex Style

@conference{dmmlacs21,
author={Melih Turhanlar and Cengiz Acartürk},
title={Detecting Turkish Phishing Attack with Machine Learning Algorithm},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,},
year={2021},
pages={577-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010717900003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - Detecting Turkish Phishing Attack with Machine Learning Algorithm
SN - 978-989-758-536-4
AU - Turhanlar M.
AU - Acartürk C.
PY - 2021
SP - 577
EP - 584
DO - 10.5220/0010717900003058