An Intelligent Prediction of Phishing URLs Using ML Algorithms
Bibliographic record
Abstract
History shows that, several cloned and fraudulent websites are developed in the World Wide Web to imitate legitimate websites, with the main motive of stealing sensitive important informational and economic resources from web surfers and financial organizations. This is a type of phishing attack, and it has cost the online networking community and all other stakeholders thousands of million Dollars. Hence, efficient counter measures are required to detect phishing URLs accurately. Machine learning algorithms are very popular for all types of data analysis and these algorithms are depicting good results in battling with phishing when we compare with other classic anti-phishing approaches, like cyber security awareness workshops, visualization approaches giving some legal countermeasures to these cyber-attacks. In this research work authors investigated different Machine Learning techniques applicability to identify phishing attacks and distinguishes their pros and cons. Specifically, various types of Machine Learning techniques are applied to reveal diverse approaches which can be used to handle anti-phishing approaches. In this work authors have experimentally compared large number of ML techniques on different phishing datasets by using various metrics. The main focus in this comparison is to showcase advantages and disadvantages of ML predictive models and their actual performance in identifying phishing attacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".