Datasets¶
Overview¶
The datasets are collections of data used for training and evaluating the model. It is crucial to have well-structured datasets to ensure the development process is effective and efficient.
Structure¶
The datasets are organized into the following directories:
raw/: Contains the raw, unprocessed data.processed/: Contains the well-structured processed data ready for training.scripts/: Contains scripts for data processing and augmentation.
Data Sources¶
Classic datasets:
Assassin (Apache Open-Source Dataset, 2005)
CEAS_08 (University of Waterloo, 2008)
Enron (Carnegie Mellon University, 2000 - 2015)
Nazario (José Nazario, 2005 - 2024)
Nigerian (1998 - 2007)
TREC (University of Waterloo, 2007)
Improved datasets:
The improved datasets are enhanced and optimized based on the classic datasets. In fact, they are still subsets of the classic datasets and do not include more recent phishing patterns.
Processing¶
Data processing is completed in two steps. The first step is performed in the datasets directory using scripts, where the raw datasets are cleaned and extracted, and organized into a well-structured, class-balanced format. The second step is carried out in the corresponding dataset’s LightningDataModule, which handles tokenization, collation, and other operations, ultimately generating a DataLoader for model training and testing.
Dataset-specific Information¶
WARNING! Corpus may contain viruses, fraudulent solicitations, and other files that may pose a security risk. Do not view any files in the folder with an ordinary browser or email client. Also note that virus or adware removal tools may damage the corpus.
Classic Datasets¶
Kaggle all-in-one solution link.
Assassin (Apache Open-Source Dataset, 2005)¶
This dataset is contributed by the internet community and is one of the earliest spam email datasets, covering a variety of topics.
spam: 500 spam messages, all received from non-spam-trap sources.
easy_ham: 2500 non-spam messages. These are typically quite easy to differentiate from spam, since they frequently do not contain any spammish signatures (like HTML etc).
hard_ham: 250 non-spam messages which are closer in many respects to typical spam: use of HTML, unusual HTML markup, coloured text, “spammish-sounding” phrases etc.
easy_ham_2: 1400 non-spam messages. A more recent addition to the set.
spam_2: 1397 spam messages. Again, more recent.
Total count: 6047 messages, with about a 31% spam ratio.
CEAS_08 (University of Waterloo, 2008)¶
This dataset comes from the CEAS 2008 email detection challenge. All emails during the competition were collected in real time and cover a wide range of topics. The structure of the original dataset is relatively complex. You can use the existing CEAS_08 dataset available on Kaggle, such as link. 39154 samples in total, 17312 (44.2%) benign, 21842 (55.8%) spam.
Enron (Carnegie Mellon University, 2000 - 2015)¶
This dataset is a collection of emails from the Enron Corporation, which was involved in a major corporate scandal. Kaggle version can be found at link. 517401 samples in total.
Nazario (José Nazario, 2005 - 2024)¶
This dataset is a collection of phishing emails curated by José Nazario over nearly two decades. Its most notable feature is that it is entirely constructed from phishing emails received in his personal inbox.
Nigerian (1998 - 2007)¶
These “Nigerian” datasets typically focus on “Nigerian fraud emails,” that is, phishing and scam emails, rather than the more common advertising or junk mail types found in spam/ham classification.