F5 ASM - Compact Learning

No, Compact mode shouldn't remove wildcard parameter.

The difference between Selective and Compact mode is that in Selective mode we always add new entity in case of no match with pure wildcard settings, while in Compact mode we add only most common used entities, while for all other entities we try to adjust pure wildcard settings (if possible).

So, e.g. in Selective mode you can get situation when you will see 50 suggestions to add new entities with score 10% (long tail) and 3 suggestions to add new entities with score 100% (most common) in Traffic learning - in such case policy will be changed by adding 3 new entities and no changes for long tail until 100% per each suggestion in it.

For the same case in Compact mode you will see only suggestions to add new entities with score 100% (most common) in Traffic learning , so policy will be changed by adding 3 new entities, but in addition to it, pure wildcard settings can be changed too adjust long tail false positives... e.g. pure wildcard settings will be adjusted in way to resolve 40 from 50 suggestions without any additional false-positives.

Compact mode just provides way to maintain policy with less entities - maintain only most common entities as dedicated entities, while all other entities, which occurs in traffic not so often should be maintained as match with pure wildcard.

Thanks, Ivan