Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Fuzzy Systems and Control (JFSC)
Evans Fubara Jumbo
Edwin Clark University
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek Joy Agboi; Frances Uche Emordi; Christopher Chukwufunaya Odiakaose; Rebecca Okeoghene Idama; Evans Fubara Jumbo; Amanda Enaodona Oweimieotu; Peace Oguguo Ezzeh; Andrew Okonji Eboka; Anne Odoh; Eferhire Valentine Ugbotu; Paul Avwerosuoghene Onoma; Arnold Adimabua Ojugo; Tabitha Chukwudi Aghaunor; Amaka Patience Binitie; Christopher Chukwudi Onochie; Patrick Ogholuwarami Ejeh; Blessing Uche Nwozor
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.325

Abstract

The widespread proliferation of smartphones has advanced portability, data access ease, mobility, and other merits; it has also birthed adversarial targeting of network resources that seek to compromise unsuspecting user devices. Increased susceptibility was traced to user's personality, which renders them repeatedly vulnerable to exploits. Our study posits a stacked learning model to classify malicious lures used by adversaries on phishing websites. Our hybrid fuses 3-base learners (i.e. Genetic Algorithm, Random Forest, Modular Net) with its output sent as input to the XGBoost. The imbalanced dataset was resolved via SMOTE-Tomek with predictors selected using a relief rank feature selection. Our hybrid yields F1 0.995, Accuracy 1.000, Recall 0.998, Precision 1.000, MCC 1.000, and Specificity 1.000 – to accurately classify all 3,316 cases of its held-out test dataset. Results affirm that it outperformed benchmark ensembles. The study shows that our proposed model, as explored on the UCI Phishing Website dataset, effectively classified phishing (cues and lures) contents on websites.
Co-Authors Amaka Patience Binitie Amanda Enaodona Oweimieotu Andrew Okonji Eboka Anne Odoh Arnold Adimabua Ojugo Blessing Uche Nwozor Christopher Chukwudi Onochie Christopher Chukwufunaya Odiakaose Eferhire Valentine Ugbotu Frances Uche Emordi Joy Agboi Patrick Ogholuwarami Ejeh Paul Avwerosuoghene Onoma Peace Oguguo Ezzeh Rebecca Okeoghene Idama Tabitha Chukwudi Aghaunor