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Dataset of Malicious and Benign Webpages
- data.mendeley.com
Updated May 1, 2020
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EmailClick to copy linkLink copiedCiteAK Singh (2020). Dataset of Malicious and Benign Webpages [Dataset]. http://doi.org/10.17632/gdx3pkwp47.1Unique identifierhttps://doi.org/10.17632/gdx3pkwp47.1Dataset updatedMay 1, 2020AuthorsAK SinghLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe dataset contains extracted attributes from websites that can be used for Classification of webpages as malicious or benign. The dataset also includes raw page content including JavaScript code that can be used as unstructured data in Deep Learning or for extracting further attributes. The data has been collected by crawling the Internet using MalCrawler [1]. The labels have been verified using the Google Safe Browsing API [2]. Attributes have been selected based on their relevance [3]. The details of dataset attributes is as given below: 'url' - The URL of the webpage. 'ip_add' - IP Address of the webpage. 'geo_loc' - The geographic location where the webpage is hosted. 'url_len' - The length of URL. 'js_len' - Length of JavaScript code on the webpage. 'js_obf_len - Length of obfuscated JavaScript code. 'tld' - The Top Level Domain of the webpage. 'who_is' - Whether the WHO IS domain information is compete or not. 'https' - Whether the site uses https or http. 'content' - The raw webpage content including JavaScript code. 'label' - The class label for benign or malicious webpage.
Python code for extraction of the above listed dataset attributes is attached. The Visualisation of this dataset and it python code is also attached. This visualisation can be seen online on Kaggle [5].
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Mapping of CSD model attribute values to JSON serialized values.
- figshare.com
xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. Grandinetti (2023). Mapping of CSD model attribute values to JSON serialized values. [Dataset]. http://doi.org/10.1371/journal.pone.0225953.t006xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0225953.t006Dataset updatedMay 31, 2023Dataset provided byPLOS ONEAuthorsDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. GrandinettiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionMapping of CSD model attribute values to JSON serialized values.
The description of the attributes from the Dimension class in version 1.0 of...
- plos.figshare.com
- figshare.com
xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. Grandinetti (2023). The description of the attributes from the Dimension class in version 1.0 of the CSD model. [Dataset]. http://doi.org/10.1371/journal.pone.0225953.t001xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0225953.t001Dataset updatedJun 1, 2023AuthorsDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. GrandinettiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe description of the attributes from the Dimension class in version 1.0 of the CSD model.
The description of the attributes from the DependentVariable class in...
- figshare.com
- plos.figshare.com
xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. Grandinetti (2023). The description of the attributes from the DependentVariable class in version 1.0 of the CSD model. [Dataset]. http://doi.org/10.1371/journal.pone.0225953.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0225953.t002Dataset updatedJun 1, 2023AuthorsDeepansh J. Srivastava; Thomas Vosegaard; Dominique Massiot; Philip J. GrandinettiLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThe description of the attributes from the DependentVariable class in version 1.0 of the CSD model.
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FacebookTwitterEmailAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset contains extracted attributes from websites that can be used for Classification of webpages as malicious or benign. The dataset also includes raw page content including JavaScript code that can be used as unstructured data in Deep Learning or for extracting further attributes. The data has been collected by crawling the Internet using MalCrawler [1]. The labels have been verified using the Google Safe Browsing API [2]. Attributes have been selected based on their relevance [3]. The details of dataset attributes is as given below: 'url' - The URL of the webpage. 'ip_add' - IP Address of the webpage. 'geo_loc' - The geographic location where the webpage is hosted. 'url_len' - The length of URL. 'js_len' - Length of JavaScript code on the webpage. 'js_obf_len - Length of obfuscated JavaScript code. 'tld' - The Top Level Domain of the webpage. 'who_is' - Whether the WHO IS domain information is compete or not. 'https' - Whether the site uses https or http. 'content' - The raw webpage content including JavaScript code. 'label' - The class label for benign or malicious webpage.
Python code for extraction of the above listed dataset attributes is attached. The Visualisation of this dataset and it python code is also attached. This visualisation can be seen online on Kaggle [5].