Machine Learning Wizard - Scanned Folders

Classifying Content > Machine learning > Machine Learning Wizard - Scanned Folders

Machine Learning Wizard - Scanned Folders

Administrator Help | TRITON AP-DATA | Version 8.3.x

These documents will be scanned and used for finding similar documents or parts of documents in the future.

Field

Description

Positive examples

Browse to a folder that contains examples of the type of textual data that you want to protect, so the system can learn from them and identify similar data in traffic.

For example, if you want to protect proprietary source code written in Java, supply the path to the location of the proprietary source code.

The examples in the folder should look similar. In other words, don't include examples of all sensitive content in the same folder. Instead, create a new classifier for other types of content.

For best results, there should be at least 50 examples in this folder.

Content type

From the pull-down list, select a type that best describes the content you want to protect; this must match the type of content in your positive examples folder.

For example, select Java and C Source code if your examples contain engineering source code written in Java. This helps the system know how to interpret your data. Possible types include:

Java and C source code

Perl source code

F# source code

Patents

Software design documents

Movie manuscripts

Financial information - investments

Other

If none of the types in the drop-down list applies to your content, select Other.

Negative examples

Browse to a folder that contains examples of textual data that is similar to but does not represent the data you want to protect. This folder must be dedicated to negative examples, and it cannot be a subdirectory of the positive examples.

For example, if you are protecting proprietary source code, you might provide the location of publicly available source code. After learning, the system will create a classifier that can tell the proprietary source code apart from the non-proprietary.

Note:

If you selected "Other" in the Content Type field, you must provide either negative or all-documents examples to help the system better understand your needs.

For best results, there should be at least 50 examples in this folder.

All documents

Optional. Select this option if you do not have a dedicated negative documents folder. Instead, navigate to a folder containing all types of documents in your network and endpoint traffic, and the system will determine good negative examples for you.

The folder can contain both positive and negative examples.

The system compares your positive examples to the documents in this folder and decides which files represent negative examples.

If you select this option and provide negative examples, you improve the speed and accuracy of the classifier.

Classifying Content > Machine learning > Machine Learning Wizard - Scanned Folders