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:
{
"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 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.