CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet

Musarat Hussain; Chi Cheng; Rui Xu; Muhammad Afzal

doi:10.1016/j.ins.2023.02.039

CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet

Musarat Hussain, Chi Cheng^*, Rui Xu, Muhammad Afzal

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

42 Citations (SciVal)

Original language	English
Pages (from-to)	328-345
Number of pages	18
Journal	Information Sciences
Volume	631
DOIs	https://doi.org/10.1016/j.ins.2023.02.039
Publication status	Published (VoR) - Jun 2023

Funding

The work presented in this paper was supported in part by the National Natural Science Foundation of China under Grant nos. 62172374 and 61802354 , and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing ( KLIGIP-2022-B06 , KLIGIP-2021-B07 ).

Keywords

Convolutional neural network
Deep learning
Malicious websites
Phishing attacks
Phishing detection

Access to Document

10.1016/j.ins.2023.02.039

Cite this

@article{6d8ff4dcf90144549bb8abc097aef2b7,

title = "CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet",

keywords = "Convolutional neural network, Deep learning, Malicious websites, Phishing attacks, Phishing detection",

author = "Musarat Hussain and Chi Cheng and Rui Xu and Muhammad Afzal",

note = "Funding Information: The work presented in this paper was supported in part by the National Natural Science Foundation of China under Grant nos. 62172374 and 61802354 , and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing ( KLIGIP-2022-B06 , KLIGIP-2021-B07 ). Publisher Copyright: {\textcopyright} 2023 Elsevier Inc.",

year = "2023",

month = jun,

doi = "10.1016/j.ins.2023.02.039",

language = "English",

volume = "631",

pages = "328--345",

journal = "Information Sciences",

issn = "0020-0255",

publisher = "Elsevier",

}

TY - JOUR

T1 - CNN-Fusion

T2 - An effective and lightweight phishing detection method based on multi-variant ConvNet

AU - Hussain, Musarat

AU - Cheng, Chi

AU - Xu, Rui

AU - Afzal, Muhammad

N1 - Funding Information: The work presented in this paper was supported in part by the National Natural Science Foundation of China under Grant nos. 62172374 and 61802354 , and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing ( KLIGIP-2022-B06 , KLIGIP-2021-B07 ). Publisher Copyright: © 2023 Elsevier Inc.

PY - 2023/6

Y1 - 2023/6

KW - Convolutional neural network

KW - Deep learning

KW - Malicious websites

KW - Phishing attacks

KW - Phishing detection

UR - https://www.scopus.com/pages/publications/85149440906

UR - https://www.scopus.com/inward/citedby.url?scp=85149440906&partnerID=8YFLogxK

U2 - 10.1016/j.ins.2023.02.039

DO - 10.1016/j.ins.2023.02.039

M3 - Article

AN - SCOPUS:85149440906

SN - 0020-0255

VL - 631

SP - 328

EP - 345

JO - Information Sciences

JF - Information Sciences

ER -

CNN-Fusion: An effective and lightweight phishing detection method based on multi-variant ConvNet

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this