README.md

---
dataset_info:
  features:
  - name: potential_typosquat
    dtype: string
  - name: legitimate
    dtype: string
  - name: label
    dtype: bool
  splits:
  - name: train
    num_bytes: 1408438
    num_examples: 38000
  - name: test
    num_bytes: 75004
    num_examples: 2000
  download_size: 639859
  dataset_size: 1483442
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
# Typosquat Dataset 

## Dataset Summary

This dataset is intended for typosquatting detection within a domain corpus. It contains 40,000 labeled pairs, categorized as either typosquatted or non-typosquatted.
The data is divided into training and test splits, each maintaining a balanced distribution of positive and negative examples.

## Supported Tasks and Leaderboards

**CE training**: The primary task is binary classification, specifically detecting typosquatting domains. The dataset can be used to train a cross-encoder or other model types for binary classification.

## Languages

The dataset is multilingual, reflecting the diversity of domain names.

## Dataset Structure

### Data Instances

Each data instance in the dataset consists of two domains and a label indicating if the second domain is a typosquatted version of the first. An example from the training set:

```json
{
  "domain": "example.com",
  "sim_domain": "exarnple.com",
  "label": 1
}
```
**domain**: A string representing the legitimate domain.
**sim_domain**: A string representing a potentially typosquatted domain.
**label**: An integer (0 or 1) where 1 indicates a typosquatted domain and 0 indicates no typosquatting.

### Data Splits

The dataset is divided as follows:

| Split    | Number of Instances |Positive|Negative|
|----------|---------------------|--------|--------|
| Train    | 38000               |   50%  |   50%  |
| Test     | 2000                |   50%  |   50%  |

## Dataset Creation

### Data Generation

The domain pairs were generated using [ail-typo-squatting](https://github.com/typosquatter/ail-typo-squatting) 
Data processing includes balancing positive and negative samples to ensure even representation.

### Dataset usage

This dataset was developed to facilitate large-scale typosquatting detection for cybersecurity applications.
It supports training and evaluating binary classifiers designed to identify domains that may have been intentionally misspelled for malicious purposes.