Anvilogic/CE-Typosquat-Training-Dataset

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.