Investigating a SMOTE-Tomek Boosted Stacked Learning Scheme for Phishing Website Detection: A Pilot Study
DOI:
https://doi.org/10.62411/jcta.14472Keywords:
Ensemble learning, Feature selection, Imbalanced datasets, Machine learning, Phishing detection, SMOTE-Tomek, Stacked ensemble, XGBoostAbstract
The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.References
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Copyright (c) 2025 Eferhire Valentine Ugbotu, Frances Uchechukwu Emordi, Emeke Ugboh, Kizito Eluemunor Anazia, Christopher Chukwufunaya Odiakaose, Paul Avwerosuoghene Onoma, Rebecca Okeoghene Idama, Arnold Adimabua Ojugo, Victor Ochuko Geteloma, Amanda Enaodona Oweimieotu, Tabitha Chukwudi Aghaunor, Amaka Patience Binitie, Anne Odoh, Chris Chukwudi Onochie, Peace Oguguo Ezzeh, Andrew Okonji Eboka, Joy Agboi, Patrick Ogholuwarami Ejeh
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