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].

Overview of Data

This dataset is a data dump containing data from June 2008 to March 2013. Note that Stack Overflow originated only in June 2008. Therefore, this dump includes all the questions and answers on Stack Overflow until March 2013.

Stack Overflow provides data dumps of all user generated data, including questions asked with the list of answers, the accepted answer per question, up/down votes, favourite counts, post score, comments, and anonymized user reputation. Stack Overflow allows users to tag discussions and has a reputation-based mechanism to rank users based on their active participation and contributions.

Attribute Information

Attribute info the datasets are in xml format including questions and answers for the following topics:

* CSS
* CSS-mobile
* HTML5
* HTML5-mobile
* JavaScript
* Javascript-mobile

The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.

Context

Data set containing Tweets captured during the Nintendo E3 2018 Conference.

Content

All Twitter APIs that return Tweets provide that data encoded using JavaScript Object Notation (JSON). JSON is based on key-value pairs, with named attributes and associated values. The JSON file include the following objects and attributes:

• Tweet - Tweets are the basic atomic building block of all things Twitter. The Tweet object has a long list of ‘root-level’ attributes, including fundamental attributes such as id, created_at, and text. Tweet child objects include user, entities, and extended_entities. Tweets that are geo-tagged will have a place child object.

  • User - Contains public Twitter account metadata and describes the author of the Tweet with attributes as name, description, followers_count, friends_count, etc.

  • Entities - Provide metadata and additional contextual information about content posted on Twitter. The entities section provides arrays of common things included in Tweets: hashtags, user mentions, links, stock tickers (symbols), Twitter polls, and attached media.

  • Extended Entities - All Tweets with attached photos, videos and animated GIFs will include an extended_entities JSON object.

  • Places - Tweets can be associated with a location, generating a Tweet that has been ‘geo-tagged.’

More information here.

Acknowledgements

I used the filterStream() function to open a connection to Twitter's Streaming API, using the keywords #NintendoE3 and #NintendoDirect. The capture started on Tuesday, June 12th 04:00 am UCT and finished on Tuesday, June 12th 05:00 am UCT.

Inspiration

• Time analysis
• Try text mining!
• Cross-language differences in Twitter
• Use this data to produce a sentiment analysis
• Twitter geolocation
• Network analysis: graph theory, metrics and properties of the network, community detection, network visualization, etc.

Context

Data set containing Tweets captured during the 2018 UEFA Champions League Final between Real Madrid and Liverpool.

Content

All Twitter APIs that return Tweets provide that data encoded using JavaScript Object Notation (JSON). JSON is based on key-value pairs, with named attributes and associated values. The JSON file include the following objects and attributes:

• Tweet - Tweets are the basic atomic building block of all things Twitter. The Tweet object has a long list of ‘root-level’ attributes, including fundamental attributes such as id, created_at, and text. Tweet child objects include user, entities, and extended_entities. Tweets that are geo-tagged will have a place child object.

  • User - Contains public Twitter account metadata and describes the author of the Tweet with attributes as name, description, followers_count, friends_count, etc.

  • Entities - Provide metadata and additional contextual information about content posted on Twitter. The entities section provides arrays of common things included in Tweets: hashtags, user mentions, links, stock tickers (symbols), Twitter polls, and attached media.

  • Extended Entities - All Tweets with attached photos, videos and animated GIFs will include an extended_entities JSON object.

  • Places - Tweets can be associated with a location, generating a Tweet that has been ‘geo-tagged.’

More information here.

Acknowledgements

I used the filterStream() function to open a connection to Twitter's Streaming API, using the keyword #UCLFinal. The capture started on Saturday, May 27th 6:45 pm UCT (beginning of the match) and finished on Saturday, May 27th 8:45 pm UCT.

Inspiration

• Time analysis
• Try text mining!
• Cross-language differences in Twitter
• Use this data to produce a sentiment analysis
• Twitter geolocation
• Network analysis: graph theory, metrics and properties of the network, community detection, network visualization, etc.

Mapping of CSD model attribute values to JSON serialized values.

Context

This dataset has been prepared to carryout classification of webpages as malicious or benign.

Content

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].

References

[1] Singh, A. K., and Navneet Goyal. "MalCrawler: A crawler for seeking and crawling malicious websites." In International Conference on Distributed Computing and Internet Technology, pp. 210-223. Springer, Cham, 2017. [2] https://developers.google.com/safe-browsing [3] Singh, A. K., and Navneet Goyal. "A Comparison of Machine Learning Attributes for Detecting Malicious Websites." In 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp. 352-358. IEEE, 2019.

Inspiration

The dataset seeks to address classification of webpages using machine learning techniques.

The 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 version 1.0 of the CSD model.