Utilizing Machine Learning for dynamic content classification of URL content

1 . A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming one or more processors to perform steps of:

obtaining data from Uniform Resource Locator (URL) transactions monitored by a cloud-based system;

labeling the data for the URL transactions with a category of a plurality of categories that describe content of a page associated with the URL;

performing preprocessing of raw Hypertext Markup Language (HTML) files for the URL transactions;

extracting features from the preprocessed raw HTML files; and

creating a machine learning model based on the features, wherein the machine learning model is configured to score content associated with an unknown URL to determine a category of the plurality of categories.

2 . The non-transitory computer-readable storage medium of claim 1 , wherein the steps include

providing the machine learning model to a node in the cloud-based system for use in production.

3 . The non-transitory computer-readable storage medium of claim 1 , wherein the obtaining data includes

obtaining big data for transactions in the cloud-based system; and

selecting URLs in the big data for transactions for websites relevant to specific categories of the plurality of categories.

4 . The non-transitory computer-readable storage medium of claim 1 , wherein the labeling the data includes

running scripts on the data and utilizing human-based verification.

5 . The non-transitory computer-readable storage medium of claim 1 , wherein the preprocessing includes

removing items in the raw HTML files that are irrelevant to feature extraction.

6 . The non-transitory computer-readable storage medium of claim 5 , wherein the items include any of special characters, HTML tags, numbers, location information, date information, header and footer date, and frequent words with little information content.

7 . The non-transitory computer-readable storage medium of claim 1 , wherein the extracting features include

calculating Term Frequency (TF) and Inverse Document Frequency (IDF) on the preprocessed raw HTML files;

ranking words in order of importance from the calculating; and

gathering important features from the ranked words.

8 . The non-transitory computer-readable storage medium of claim 1 , wherein the gathering important features utilizes any of reverse feature elimination, selectKbest, and a support vector machine model.

9 . The non-transitory computer-readable storage medium of claim 1 , wherein the machine learning model is Light Gradient Boosted Machine (LightGBM).

10 . A method comprising:

obtaining data from Uniform Resource Locator (URL) transactions monitored by a cloud-based system;

labeling the data for the URL transactions with a category of a plurality of categories that describe content of a page associated with the URL;

performing preprocessing of raw Hypertext Markup Language (HTML) files for the URL transactions;

extracting features from the preprocessed raw HTML files; and

creating a machine learning model based on the features, wherein the machine learning model is configured to score content associated with an unknown URL to determine a category of the plurality of categories.

11 . The method of claim 10 , further comprising

providing the machine learning model to a node in the cloud-based system for use in production.

12 . The method of claim 10 , wherein the obtaining data includes

obtaining big data for transactions in the cloud-based system; and

selecting URLs in the big data for transactions for websites relevant to specific categories of the plurality of categories.

13 . The method of claim 10 , wherein the labeling the data includes

running scripts on the data and utilizing human-based verification.

14 . The method of claim 10 , wherein the preprocessing includes

removing items in the raw HTML files that are irrelevant to feature extraction.

15 . The method of claim 14 , wherein the items include any of special characters, HTML tags, numbers, location information, date information, header and footer date, and frequent words with little information content.

16 . The method of claim 10 , wherein the extracting features include

calculating Term Frequency (TF) and Inverse Document Frequency (IDF) on the preprocessed raw HTML files;

ranking words in order of importance from the calculating; and

gathering important features from the ranked words.

17 . The method of claim 10 , wherein the gathering important features utilizes any of reverse feature elimination, selectKbest, and a support vector machine model.

18 . The method of claim 10 , wherein the machine learning model is Light Gradient Boosted Machine (LightGBM).

19 . A node in a cloud-based network comprising:

one or more processors; and

memory storing instructions that, when executed, cause the one or more processors to obtain data from Uniform Resource Locator (URL) transactions monitored by a cloud-based system;

label the data for the URL transactions with a category of a plurality of categories that describe content of a page associated with the URL;

perform preprocessing of raw Hypertext Markup Language (HTML) files for the URL transactions;

extract features from the preprocessed raw HTML files; and

create a machine learning model based on the features, wherein the machine learning model is configured to score content associated with an unknown URL to determine a category of the plurality of categories.

20 . The node of claim 19 , wherein the node is configured to provide the machine learning model to one or more additional nodes in the cloud-based system for use in production.