Detecting DNS Typo-Squatting Using Ensemble-Based Feature Selection & Classification Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The domain name system (DNS) is a crucial component in the current IP-based Internet architecture. However, it suffers from several security vulnerabilities. This is because it does not have proper data integrity and origin authentication mechanisms. This article focuses on the typo-squatting vulnerability (a vulnerability often neglected). Typo-squatting is when attackers register a domain name that is extremely similar to an existing one to redirect users to malicious/suspicious websites. This can lead to information threats, corporate secret leakage, and can facilitate fraud. As an extension to our previous work, this work proposes ensemble-based feature selection and classification models to detect DNS typo-squatting attacks with low complexity. It is shown through experiments that the proposed framework detects the malicious/suspicious typo-squatting domains with high accuracy (above 87%). More specifically, the proposed model only loses between 0.9% and 1.5% in accuracy, 5% in precision (reaching 88%), and around 8% in recall (reaching 93%) while having a lower computational complexity given that the feature set size is reduced by more than 50%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it