A new phishing-website detection framework using ensemble classification and clustering
Bibliographic record
Abstract
Phishing websites are characterized by distinguished visual, address, domain, and embedded features, which identify and defend such threats. Yet, phishing website detection is challenged by overlapping these features with legitimate websites’ features. As the inter-class variance between legitimate and phishing websites becomes low, commonly utilized machine learning algorithms suffer from low performance in overlapping feature cases. Alternatively, ensemble learning that combines multiple predictions intending to address low inter-class variations in the classified data improves the performance in such cases. Ensemble learning utilizes multiple classifiers of similar or different types with multiple deviations of the training data. This paper develops a framework based on random forest ensemble techniques. The limitations of the random forest are the inability to capture the high correlation between features and their join dependency on the label. The random forest is combined with k-means clustering to capture the feature correlation. The framework is evaluated for phishing detection with a dataset of 5000 samples. The results showed the proposed framework over-performed the random forest classifier, all other ensemble classifiers, and the conventional classification algorithms. The proposed framework achieved an accuracy of 98.64%, precision of 0.986, recall of 0.987, and F-measure of 0.986.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".