Dataset card for DomainRel

README.md

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-license: cc-by-4.0
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+license: cc-by-4.0
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+
+# Dataset Card for *Domains by Reliability (DomainRel)*
+
+
+DomainRel is a large-scale dataset of web domains labelled binarily as per their overall reliability level. 
+The domains span areas of general knowledge, (mis)information, phishing and malware. 
+DomainRel is an aggregate and reconstruction of existing datasets originating from each of these specific areas, offering the first cross-domain dataset of this type and scale. 
+
+
+## Dataset Details
+
+DomainRel can be downloaded in its simplest form (`labels.csv`), which contains 674,737 with a binary reliability score with the following class distribution:
+```
+Unreliable (0): 361,537
+Reliable (1): 313,199
+```
+
+An alternative `labels_annot.csv` is also available; it contains the same domain data, with disaggregated per-source scores tracing the provenance of each datapoint as per its original data source. 
+
+- **Curated by** the CrediNet organisation, which consists of a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA.
+- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
+*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
+Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
+
+
+### Dataset Sources 
+
+Refer to the below for a more detailed breakdown of the dataset's class distribution in terms of dataset of origin. 
+
+
+**Total:**
+
+```bash
+  0: 361537
+  1: 313199
+```
+
+which consists of the following datasets: 
+
+### LegitPhish
+
+[LegitPhish](https://data.mendeley.com/datasets/hx4m73v2sf/2) is a URL-based dataset with binary labels (0 = Phishing, 1 = Legitimate). The data is processed by converting URLs to their domain name, averaging the scores of all URLs that resolve to the same domain, and then binarizing based on a threshold of 0.5. We also standardise the header (we always use `domain` and `label` in the processed csvs.)
+
+```bash
+0: 26957
+1: 37113
+```
+
+### PhishDataset
+
+[PhishDataset](https://github.com/ESDAUNG/PhishDataset/blob/main/data_imbal%20-%2055000.xlsx) is a URL-based dataset with binary labels (0 = Legitimate, 1 = Phishing)
+
+```bash
+0: 3730
+1: 40535
+```
+
+### Nelez
+
+[Nelež](https://www.nelez.cz) is a blacklist of misinformation websites from a Czech organisation of the same name.
+
+```bash
+0: 51
+1: 0
+```
+
+### Wikipedia
+
+[Wikipedia](https://github.com/kynoptic/wikipedia-reliable-sources) is an aggregate set of a reliability ratings from multiple Wikipedia sources. The data is processed to keep only 'boosted' and 'discarded' domains (corresponding to 1 and 0 respectively -- they also have a neutral category we discard).
+
+```bash
+0: 1029
+1: 2906
+```
+
+### URL-Phish
+
+[URL-Phish](https://data.mendeley.com/datasets/65z9twcx3r/1) is a feature-engineered dataset for phishing detection. URLs have label 0 if they are considered benign, 1 for phishing.
+
+```bash
+0: 10551
+1: 92460
+```
+
+### Phish & Legit
+
+[Phish & Legit](https://www.kaggle.com/datasets/harisudhan411/phishing-and-legitimate-urls?resource=download) is a URL Classification dataset of suspicous (0) and genuine (1) web addresses.
+
+```bash
+0: 292163
+1: 146316
+```
+
+### Misinformation domains
+
+[Misinfo-domains](https://github.com/JanaLasser/misinformation_domains/tree/main) is a collection of domains labelled unreliable if they are assessed as spreaders of unreliable information.
+
+```bash
+0: 2170
+1: 2597
+```
+
+### Malware domains
+
+[URLHaus](https://urlhaus.abuse.ch) is a collection of malware domains.
+
+```bash
+0: 31540
+1: 0
+```
+
+## Dataset Card Contact
+
+[Emma Kondrup](mailto:emma.kondrup@mila.quebec)