The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study
Bibliographic record
Abstract
In this study, we aimed to identify spatial clusters of countries with high rates of cyber attacks directed at other countries. The cyber attack dataset was obtained from Canadian Institute for Cybersecurity, with over 110,000 Uniform Resource Locators (URLs), which were classified into one of 5 categories: benign, phishing, malware, spam, or defacement. The disease surveillance software SaTScanTM was used to perform a spatial analysis of the country of origin for each cyber attack. It allowed the identification of spatial and space-time clusters of locations with unusually high counts or rates of cyber attacks. Number of internet users per country obtained from the 2016 CIA World Factbook was used as the population baseline for computing rates and Poisson analysis in SaTScanTM. The clusters were tested for significance with a Monte Carlo study within SaTScanTM, where any cluster with p < 0.05 was designated as a significant cyber attack cluster. Results using the rate of the different types of malicious URL cyber attacks are presented in this paper. This novel approach of studying cyber attacks from a spatial perspective provides an invaluable relative risk assessment for each type of cyber attack that originated from a particular country.
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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 | 0.000 | 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 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".