OFMCDM/IRF: A Phishing Website Detection Model based on Optimized Fuzzy Multi-Criteria Decision-Making and Improved Random Forest
Bibliographic record
Abstract
With increasing social and financial activities on the web, phishing has become one of the most critical threats in cybersecurity. Many methods have been proposed to identify phishing websites, such as fuzzy logic, neural networks, data mining, heuristic-based phishing detection, and machine learning. On the other hand, phishers develop more sophisticated techniques, decreasing the efficacy of the existing methods. This paper proposes a phishing detection model based on optimized Fuzzy Multi-Criteria Decision-Making (OFMCDM) and Improved Random Forest (IRF). The model utilizes Uniform Resource Locator (URL) and Hypertext Markup Language (HTML) features to prevent sharing users’ sensitive information such as username, password, social security, or credit card number. Our experiments show competitive results from our models compared to existing models, including Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".