What syntax is used for defining patterns in ML exclusions?

The syntax used for defining patterns in machine learning (ML) exclusions is GLOB SYNTAX. GLOB syntax allows for the use of wildcard characters to create patterns that can match filenames or pathnames in a flexible way. This is particularly useful in specifying exclusions when you want certain files or directories to be ignored by the machine learning models, for example, when training to detect threats or analyze behavioral patterns. GLOB patterns can match paths using wildcards like asterisks (*) for multiple characters and question marks (?) for single characters, thus providing a straightforward method for pattern matching in various file systems.

While other syntax options like REGEX SYNTAX, JSON SYNTAX, and XML SYNTAX exist, they serve different purposes in programming and data representation. Regex, or regular expressions, is powerful for complex string matching and is often used in search operations but is not typically used for the simpler pathname pattern recognition intended with GLOB. JSON and XML are structured formats used for data interchange and representation rather than for defining patterns in exclusions.