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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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tags: |
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- phishing |
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- url |
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- html |
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- text |
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--- |
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# Phishing Dataset |
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Phishing dataset compiled from various resources for classification and phishing detection tasks. |
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## Dataset Details |
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The dataset has two columns: `text` and `label`. Text field contains samples of: |
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- URL |
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- SMS messages |
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- Email messages |
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- HTML code |
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Which are labeled as **1 (Phishing)** or **0(Benign)**. |
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### Source Data |
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This dataset is a compilation of 4 sources, which are described below: |
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- [Mail dataset](https://www.kaggle.com/datasets/subhajournal/phishingemails) that specifies the body text of various emails that can be used to detect phishing emails, |
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through extensive text analysis and classification with machine learning. Contains over 18,000 emails |
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generated by Enron Corporation employees. |
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- [SMS message dataset](https://data.mendeley.com/datasets/f45bkkt8pr/1) of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages |
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and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used |
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to classify messages as malicious or legitimate. The data was collected by converting images obtained from |
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the Internet into text using Python code. |
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- [URL dataset](https://www.kaggle.com/datasets/harisudhan411/phishing-and-legitimate-urls) with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are |
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phishing domains. It is a collection of data samples from various sources, the URLs were collected from the |
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JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and |
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some open source databases, including Excel files. |
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- [Website dataset](https://data.mendeley.com/datasets/n96ncsr5g4/1) of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each |
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instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple |
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keyword search on the Google search engine was used and the first 5 URLs of each search were collected. |
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Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse |
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collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset |
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repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo. |
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#### Dataset Processing |
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Primarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has |
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not been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification. |
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_Is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_ |
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In general, **NO**. Preprocessing will not change the output predictions. In fact, removing empty words (which |
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are considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will |
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worsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these "stop words" |
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are valuable information for BERT. The same goes for punctuation: a question mark can certainly change the |
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overall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean |
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eliminating the context that BERT could have used to get better results. |
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However, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should |
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be considered. That is at the discretion of whoever wishes to employ this dataset. |
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For more information check these links: |
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- https://stackoverflow.com/a/70700145 |
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- https://datascience.stackexchange.com/a/113366 |
