PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks

Tristan Bilot; Grégoire Geis; Badis Hammi

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks

Topics: Machine Learning and AI Security; Network Security; Security Management; Security Metrics and Measurement; Security Protocols

In Proceedings of the 19th International Conference on Security and Cryptography SECRYPT - Volume 1, 428-435, 2022 , Lisbon, Portugal

Authors: Tristan Bilot ; Grégoire Geis and Badis Hammi

Affiliation: EPITA School of Engineering, France

Keyword(s): Phishing Detection, Graph Neural Networks, Deep Learning, Cybersecurity.

Abstract: Because of the importance of the web in our daily lives, phishing attacks have been causing a significant damage to both individuals and organizations. Indeed, phishing attacks are today among the most widespread and serious threats to the web and its users. Currently, the main approaches deployed against such attacks are blacklists. However, the latter represent numerous drawbacks. In this paper, we introduce PhishGNN, a Deep Learning framework based on Graph Neural Networks, which leverages and uses the hyperlink graph structure of websites along with different other hand-designed features. The performance results obtained, demonstrate that PhishGNN outperforms state of the art results with a 99.7% prediction accuracy.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 18.216.81.181

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Bilot, T., Geis, G. and Hammi, B. (2022). PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks. In Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 428-435. DOI: 10.5220/0011328600003283

@conference{secrypt22,
author={Tristan Bilot and Grégoire Geis and Badis Hammi},
title={PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT},
year={2022},
pages={428-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011328600003283},
isbn={978-989-758-590-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks
SN - 978-989-758-590-6
IS - 2184-7711
AU - Bilot, T.
AU - Geis, G.
AU - Hammi, B.
PY - 2022
SP - 428
EP - 435
DO - 10.5220/0011328600003283
PB - SciTePress