Web page risk analysis using machine learning

An apparatus comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
• receive, from a remote server, a risk detection machine learning model trained to receive user input data associated with a web page and output an indication of risk associated with the web page;
• execute a web browser application comprising a risk detection web browser plugin configured to implement the risk detection machine learning model;
• collect user activity data that indicates a plurality of different applications executing on the apparatus by:
• detecting the plurality of different applications, other than the web browser application, executing on the apparatus;
• access, via the web browser application, a first web page;
• collect, via the risk detection web browser plugin, page data associated with the first web page, wherein the page data comprises one or more words displayed by the web page;
• query, based on the page data, the remote server to identify a merchant associated with the web page;
• calculate, using the risk detection machine learning model, a risk level of the first web page by processing;
• the user activity data that indicates the plurality of different applications executing on the apparatus, and
• a quantity of fraudulent transactions associated with the merchant;
• cause, based on the risk level satisfying a threshold and based on the one or more words of the page data, generation of a temporary credit card number;
• and
• cause output of the temporary credit card number for use with the first web page.

One or more non-transitory computer-readable storage media having computer-executable instructions stored thereon that, when executed by one or more processors, cause a computing device to perform steps comprising:

• receiving, by the computing device and from a remote server, a risk detection machine learning model trained to receive user input data associated with a web page and output an indication of risk associated with the web page;
• executing, by the computing device, a web browser application comprising a risk detection web browser plugin configured to implement the risk detection machine learning model;
• collecting user activity data that indicates a plurality of different applications executing on the computing device by:
• detecting the plurality of different applications, other than the web browser application, executing on the computing device;
• accessing, via the web browser application, a first web page;
• collecting, via the risk detection web browser plugin, page data associated with the first web page, wherein the page data comprises one or more words displayed by the web page;
• querying, based on the page data, the remote server to identify a merchant associated with the web page;
• calculating a risk level of the first web page by processing, by the computing device and using the risk detection machine learning model;
• the user activity data that indicates the plurality of different applications executing on the computing device, and
• a quantity of fraudulent transactions associated with the merchant;
• causing, based on the risk level satisfying a threshold and based on the one or more words of the page data, generation of a temporary credit card number;
• and
• causing output of the temporary credit card number for use with the first web page.