PHISHING AND SPAM DETECTION: BASED ON URL HEURISTICS AND EMAIL TEXT ANALYSIS
Bibliographic record
Abstract
Phishing attacks continue to compromise cybersecurity by exploiting deceptive URLs and fraudulent emails to extract confidential user information.Traditional systems relying on static heuristics and blacklists are challenged by novel phishing tactics-especially the use of dynamically generated session URLs and subtle email cues.In this paper, we propose a dualmodel approach that integrates URL-based heuristics with email text analysis using machine learning (ML) and deep learning (DL) techniques.The system extracts lexical and hostbased features from URLs and leverages natural language processing (NLP) to analyze email messages.Experiments on an 11,054-sample phishing URL dataset and a 5,572-sample email dataset reveal that our method achieves a URL classification accuracy of 96.8% and an email spam detection accuracy of 99.2%, with a combined system accuracy of 98.5%.These results demonstrate the robustness of the integrated approach in addressing challenges such as flagging new links and handling dynamic URL patterns.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".