Phishing detection using machine learning algorithm

Jibrilla Tanimu; Stavros Shiaeles

doi:10.1109/CSR54599.2022.9850316

Phishing detection using machine learning algorithm

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The rapid increase of phishing attacks has led individuals and organizations losing billions of dollars as well as worried about the confidentiality and privacy of their data. This tremendous annual increase of phishing attacks shows that the current detection methods available are not sufficient, therefore more effective phishing detection methods should be developed. This paper proposed a novel phishing detection model using machine learning, to improve efficacy and accuracy in phishing detection. This paper explores the current state-of-the-art in phishing detection along with their drawbacks and proposes a new novel method based on image visualisation of website code and features extraction from malicious URLs which is under development.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	317-322
Number of pages	6
ISBN (Electronic)	9781665499521
ISBN (Print)	9781665499538
DOIs	https://doi.org/10.1109/CSR54599.2022.9850316
Publication status	Published - 6 Sept 2022
Event	2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022 - Virtual, Online, Greece Duration: 27 Jul 2022 → 29 Jul 2022

Conference

Conference	2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022
Country/Territory	Greece
City	Virtual, Online
Period	27/07/22 → 29/07/22

Keywords

Binary visualisations
Images
Machine learning
Phishing detection
Spam

Access to Document

10.1109/CSR54599.2022.9850316

Cite this

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title = "Phishing detection using machine learning algorithm",

abstract = "The rapid increase of phishing attacks has led individuals and organizations losing billions of dollars as well as worried about the confidentiality and privacy of their data. This tremendous annual increase of phishing attacks shows that the current detection methods available are not sufficient, therefore more effective phishing detection methods should be developed. This paper proposed a novel phishing detection model using machine learning, to improve efficacy and accuracy in phishing detection. This paper explores the current state-of-the-art in phishing detection along with their drawbacks and proposes a new novel method based on image visualisation of website code and features extraction from malicious URLs which is under development. ",

keywords = "Binary visualisations, Images, Machine learning, Phishing detection, Spam",

author = "Jibrilla Tanimu and Stavros Shiaeles",

note = "No ISSN; 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022 ; Conference date: 27-07-2022 Through 29-07-2022",

year = "2022",

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day = "6",

doi = "10.1109/CSR54599.2022.9850316",

language = "English",

isbn = "9781665499538",

pages = "317--322",

booktitle = "Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Tanimu, J & Shiaeles, S 2022, Phishing detection using machine learning algorithm. in Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022. Institute of Electrical and Electronics Engineers Inc., pp. 317-322, 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022, Virtual, Online, Greece, 27/07/22. https://doi.org/10.1109/CSR54599.2022.9850316

TY - GEN

T1 - Phishing detection using machine learning algorithm

AU - Tanimu, Jibrilla

AU - Shiaeles, Stavros

N1 - No ISSN

PY - 2022/9/6

Y1 - 2022/9/6

N2 - The rapid increase of phishing attacks has led individuals and organizations losing billions of dollars as well as worried about the confidentiality and privacy of their data. This tremendous annual increase of phishing attacks shows that the current detection methods available are not sufficient, therefore more effective phishing detection methods should be developed. This paper proposed a novel phishing detection model using machine learning, to improve efficacy and accuracy in phishing detection. This paper explores the current state-of-the-art in phishing detection along with their drawbacks and proposes a new novel method based on image visualisation of website code and features extraction from malicious URLs which is under development.

AB - The rapid increase of phishing attacks has led individuals and organizations losing billions of dollars as well as worried about the confidentiality and privacy of their data. This tremendous annual increase of phishing attacks shows that the current detection methods available are not sufficient, therefore more effective phishing detection methods should be developed. This paper proposed a novel phishing detection model using machine learning, to improve efficacy and accuracy in phishing detection. This paper explores the current state-of-the-art in phishing detection along with their drawbacks and proposes a new novel method based on image visualisation of website code and features extraction from malicious URLs which is under development.

KW - Binary visualisations

KW - Images

KW - Machine learning

KW - Phishing detection

KW - Spam

UR - http://www.scopus.com/inward/record.url?scp=85137344671&partnerID=8YFLogxK

UR - https://www.ieee-csr.org/archive/2022/

U2 - 10.1109/CSR54599.2022.9850316

DO - 10.1109/CSR54599.2022.9850316

M3 - Conference contribution

AN - SCOPUS:85137344671

SN - 9781665499538

SP - 317

EP - 322

BT - Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022

Y2 - 27 July 2022 through 29 July 2022

ER -

Phishing detection using machine learning algorithm

Abstract

Conference

Keywords

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