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The emails in the TExtPhish Email Collection Corpus are under the license of cc-by-nc-nd-4.0, and their use is governed by the following agreements:
- You agree to not distribute or reproduce any derivatives, in whole or in part, any document from the Collection.
- You agree to not attempt to identify, or speculate on the identity of, any individual in TExtPhish Collection, even if that information is available from public sources.
- Re-use of this data is also subject to Reddit API terms which includes:
- not encouraging or promoting illegal activity.
- not using this dataset with the intent of introducing any viruses, worms, defects, Trojan horses, malware, or any other items of a destructive nature.
- no selling, leasing, or sublicensing this data whether for direct commercial or monetary gain.
In the event that End User violates the terms of this agreement, then upon notice from the dataset maintainers, end users shall cease use of the collection and destroy all copies of the collection and other documents that contain excerpts from the Collection.
We would like to keep track of this dataset users for statistics purposes (how many users and affiliations) and agreement only.
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Dataset Card for TExtPhish
Dataset Description
Dataset Summary
This dataset card aims to describe the TExtPhish collection and its intended use.
Languages
The current version only includes data samples in English, as spoken partially by Reddit users on the r/Scams blackmail subreddits. In the Future, we would like to explore more in different languages. Collaborators are encouraged to contact the authors to extend the current version with more diverse extortion emails in different languages.
Dataset Structure
Initial Data Collection and Sanitization
First, we select benign samples from the publicly available dataset, such as Enron and SpamAssassin. We extract each email from email threads and tokenize personally sensitive information using name entity recognition, regular expression and synthetically replaced information.
Second, we collect extortion attacks from reddit posts |r/Scams and botnet ransomware emails from |Malware Traffic Analysis repository. We remove unecessary comment from the reddit thread and we only keep extortion emails.
To make the dataset challenging, we keep only the most semantically similar benign emails to the extortion attacks. For semantic textual similarity, we first applied sentence transformers (SBERT) to get contextual sentence embeddings of benign and extortion samples. Then, we apply the Facebook AI Similarity Search (FAISS) measure to search for similar benign instances to extortion attacks.
Data Instances
| Extortion Class | Examples from Sentence-level subset |
|---|---|
| Blackmail | - I will delete the corresponding recording and I will not blackmail you ever again. |
| Ransomware | - Tap to Download Attachment Xinalink_servicescom (10.3 KB). |
| Sextortion | - In case you ignore me, within 96 h, ur sex tape will be posted on the net. |
Data Sources
The following tables describe the data sources used to generate this dataset.
- Extortion Data
| Source | Total number of Emails | Total number of Sentences |
|---|---|---|
| r/Scams Extortion Emails | 1,113 | 17,393 |
| Botnet Ransomware Emails | 150 | 1,510 |
- Benign Data
| Source | Total number of Emails | Total number of Sentences |
|---|---|---|
| Enron | 1,360 | 26,835 |
| SpamAssasin | 1,010 | 12,348 |
Data Fields
The dataset is structered as follow:
list[{
"src": str, # Data source (e.g, SpamAssassin, Enron, Reddit)
"content": str, # Content (sentence-level or email-level)
"label": str, # Extortion label (blackmail, ransomware, sextortion) or benign label
}]
Loading TExtPhish Dataset
To load the email-level subset, use the following instructions:
email_subset = load_dataset("TExtPhish/TExtPhish", data_dir="email-level", split="train", sep=";")
To load the sentence-level subset, use the following instructions:
sentence_subset = load_dataset("TExtPhish/TExtPhish", data_dir="sentence-level", split="train", sep=";")
To load the Homograph-Perturbed subset on sentences, use the following instructions:
homograph_subset = load_dataset("TExtPhish/TExtPhish", data_dir="homograph-perturbed-sentences", split="train", sep=";")
Splitting TExtPhish Dataset
If you would like to load the dataset under cross validation setting, you can load (train or test) which will be divided into k folds (example below k=10).
test_folds = load_dataset('TExtPhish/TExtPhish', split=[f"train[{k}%:{k+10}%]" for k in range(0, 100, 10)], data_dir="sentence-level", sep=';')
train_folds = load_dataset('TExtPhish/TExtPhish',split=[f"train[:{k}%]+train[{k+10}%:]" for k in range(0, 100, 10)], data_dir="sentence-level", sep=';')
This easy and ready-to-use divided folds consist of dividing randomly TExtPhish into k=10 parts. Nine of these parts are used for training while one tenth is reserved for testing. This procedure will be repeated k=10 times each time reserving a different tenth for testing. In other words, each testing set is a 10% chunk, and the training set makes up the remaining complementary 90% chunk.
Binarize Labels
from sklearn.preprocessing import LabelEncoder
# Transforming text labels to encoded labels using the MultiLabelBinarizer
multibin = LabelEncoder()
Y_train = multibin.fit_transform(Y_train)
Y_test = multibin.fit_transform(Y_test)
Personal and Sensitive Information
We ensure to remove any personal and sensitive information before uploading our dataset. The emails provided in this corpus are stripped from sensitive information that are replaced with tokens (e.g., url_token), synthetically replaced, or originally obfuscated (***) in order to anonymize the data.
Considerations for Using the Data
Intended Uses
Our collection may only be used for linguistic non-profit research including but not limited to Information Retrieval, Text Classification, Natural Language Processing, Machine Learning, Phishing Detection, Data Privacy and Security, and like fields.
Social Impact of Dataset
Users are totally responsible for any misuse of the dataset that goes against the original intended use of this dataset. The extortion dataset should not be used for any harmful means to institute and propagate attacks.
Positive Social Impact
- Researchers can use TExtPhish to study the tactics and techniques used by attackers, identify vulnerabilities, and develop effective countermeasures against extortion.
- Educators can use TExtPhish to teach students about online safety, how to recognize phishing extortion attempts, and best practices for protecting personal information and financial loss.
- Cybersecurity professionals can use TExtPhish to train machine learning models to detect and block phishing emails with money extortion attempts, improving incident response strategies, and minimizing financial loss exposure.
Negative Social Impact
- Attackers might use TExtPhish to create automatic botnets that generate better extortion attacks.
- Attackers might use TExtPhish to propagate deception and propaganda online.
- Attackers might attempt to use TExtPhish as an initializing phase to perform malware, ransomware, or embed trojans within a targeted system to gain remote access.
Additional Information
Licensing Information
As the maintainers of this dataset, we choose to follow licensing Attribution- NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) to ensure that the dataset is non-commercial and it cannot be distributed or reproduced, in whole or in part, any document from the Collection. A portion of our dataset was downloaded using Reddit's API Wrapper through the PRAW package for the python programming language. Re-use of this data is subject to Reddit API terms, which include:
- Users shall not encourage or promote illegal activity throughout the use of this dataset.
- Users shall not use this dataset with the intent of introducing any viruses, worms, defects, Trojan horses, malware, or any other items of a destructive nature.
- Users shall not sell, lease, or sublicense this data whether for direct commercial or monetary gain.
Citation Information
Information about citation will soon be updated.
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