Publication result detail

Unmasking the Phishermen: Phishing Domain Detection with Machine Learning and Multi-Source Intelligence

HRANICKÝ, R.; HORÁK, A.; POLIŠENSKÝ, J.; JEŘÁBEK, K.; RYŠAVÝ, O.

Original Title

Unmasking the Phishermen: Phishing Domain Detection with Machine Learning and Multi-Source Intelligence

English Title

Unmasking the Phishermen: Phishing Domain Detection with Machine Learning and Multi-Source Intelligence

Type

Paper in proceedings (conference paper)

Original Abstract

In the digital landscape, phishing attacks have rapidly evolved into a major cybersecurity challenge, posing significant risks to individuals and organizations. This short paper presents our preliminary research on detecting phishing domains. Our approach amalgamates intelligence from multiple sources: DNS servers, WHOIS/RDAP, TLS certificates, and GeoIP data. We created a rich 15.8 GB dataset of information about benign and phishing domains, from which we derived a comprehensive 80-feature vector for training and testing machine learning classifiers. We propose preliminary results with a fine-tuned XGBoost model, achieving 0.9716 precision rate, 0.9540 F-1 score, and false positive rate of 0.23%.

English abstract

In the digital landscape, phishing attacks have rapidly evolved into a major cybersecurity challenge, posing significant risks to individuals and organizations. This short paper presents our preliminary research on detecting phishing domains. Our approach amalgamates intelligence from multiple sources: DNS servers, WHOIS/RDAP, TLS certificates, and GeoIP data. We created a rich 15.8 GB dataset of information about benign and phishing domains, from which we derived a comprehensive 80-feature vector for training and testing machine learning classifiers. We propose preliminary results with a fine-tuned XGBoost model, achieving 0.9716 precision rate, 0.9540 F-1 score, and false positive rate of 0.23%.

Keywords

Phishing, Domain, Detection, Machine learning, XGBoost, Features, DNS, RDAP, TLS, GeoIP

Key words in English

Phishing, Domain, Detection, Machine learning, XGBoost, Features, DNS, RDAP, TLS, GeoIP

Authors

HRANICKÝ, R.; HORÁK, A.; POLIŠENSKÝ, J.; JEŘÁBEK, K.; RYŠAVÝ, O.

RIV year

2025

Released

06.05.2024

Publisher

Institute of Electrical and Electronics Engineers

Location

Soul

ISBN

979-8-3503-2794-6

Book

Proceedings of IEEE/IFIP Network Operations and Management Symposium 2024

Pages from

1

Pages to

5

Pages count

5

URL

https://ieeexplore.ieee.org/document/10575573

BibTex

@inproceedings{BUT186776,
  author="Radek {Hranický} and Adam {Horák} and Jan {Polišenský} and Kamil {Jeřábek} and Ondřej {Ryšavý}",
  title="Unmasking the Phishermen: Phishing Domain Detection with Machine Learning and Multi-Source Intelligence",
  booktitle="Proceedings of IEEE/IFIP Network Operations and Management Symposium 2024",
  year="2024",
  pages="1--5",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Soul",
  doi="10.1109/NOMS59830.2024.10575573",
  isbn="979-8-3503-2794-6",
  url="https://ieeexplore.ieee.org/document/10575573"
}

Documents

Unmasking_the_Phishermen-PREPRINT