Evaluating Supervised Machine Learning Models for Zero-Day Phishing Attack Detection: A Comprehensive Study
Bibliographic record
Abstract
Abstract To have highly secure e-commerce websites, detecting and preventing cyber-attacks is of high importance. Among diverse types of cyber-attacks, identifying zero-day attacks is problematic since they are unknown to the security system. It is because they usually are launched by an attacker and none of the existing defined patterns match with the unknown (malicious) case. There are many machine learning models developed to analyze and detect phishing websites, specifically using supervised models. However, the main issue with zero-day attacks is that they are not seen before, so their patterns are not trained to the model. Thus, the supervised models designed for detecting phishing URLs should be very accurate in predicting the label of unseen data. This research addresses the underlying issue by evaluating seven different supervised machine learning models to assess their accuracy in predicting zero-day phishing attacks. Unlike previous studies that examined models on features that are only extracted from URLs, our evaluation framework incorporates a comprehensive dataset that includes not only URL features but also third-party extracted features as well as content-based features. This research also examines the performance of the models under the impact of dimension reduction techniques. By reducing the dimensionality of the dataset, we aim to improve computational efficiency without compromising the accuracy of the models. The results depict that XGBoost performs best on zero-day attack data sets with accuracy and an f1-score of 96.6%, and PCA can be applied in high-dimensional data sets without adverse effects on the models’ performance.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".