Anvilogic/Embedder-Typosquat-Training-Dataset

Typosquat Embedding Dataset

Dataset Summary

This dataset is designed for training embedding models to recognize typosquatting within a domain corpus. It consists of pairs of legitimate and typosquatted domains for use in similarity learning, enabling models to identify subtle domain alterations.

The dataset is formatted for embedding-based training, specifically useful for contrastive learning techniques or other tasks where domain similarity is a key factor.

Supported Tasks and Leaderboards

Embedding Training: The primary task supported by this dataset is contrastive learning to create embeddings for typosquatting detection. The dataset can be used to train a similarity model, such as a dual-encoder, where each instance is a pair of legitimate and potentially typosquatted domains.

Languages

This dataset includes a multilingual set of domains, reflecting the diversity of internet domains globally.

Dataset Structure

Data Instances

Each instance in the dataset consists of two domains:

• anchor: The legitimate domain.
• positive: A version of the domain with minor alterations that may represent typosquatting.

An example from the dataset is as follows:

{
  "anchor": "e-volution.ai",
  "positive": "e-volutiọn.ai"
}

The anchor and positive columns are both strings representing domains. The "positive" domain is a variation created by intentional typosquatting techniques (e.g., homoglyphs or character substitution).

Data Splits

The dataset is structured to be used for embedding model training and evaluation:

• Split: Train Number of Instances: 43,447
• Split: Test Number of Instances: 10,881

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 is suitable for cybersecurity applications focusing on typosquatting detection. It can be used to train and evaluate embedding-based models designed to identify domains that may have been manipulated for malicious purposes, supporting efforts in online safety and domain monitoring.