As a general rule, the higher the confidence value, the higher the
"reliability/precision" of the classification.
In other words, this new field provides an hint about "how" the flow
classification has been obtained.
For example, the application may want to ignore classification "by-port"
(they are not real DPI classifications, after all) or give a second
glance at flows classified via LRU caches (because of false positives).
Setting only one value for the confidence field is a bit tricky: more
work is probably needed in the next future to tweak/fix/improve the logic.
Improve Microsoft, GMail, Likee, Whatsapp, DisneyPlus and Tiktok
detection.
Add Vimeo, Fuze, Alibaba and Firebase Crashlytics detection.
Try to differentiate between Messenger/Signal standard flows (i.e chat)
and their VOIP (video)calls (like we already do for Whatsapp and
Snapchat).
Add a partial list of some ADS/Tracking stuff.
Fix Cassandra, Radius and GTP false positives.
Fix DNS, Syslog and SIP false negatives.
Improve GTP (sub)classification: differentiate among GTP-U, GTP_C and
GTP_PRIME.
Fix 3 LGTM warnings.
Decoding of ipv6 traffic with extension headers was completely broken,
since the beginning of the L4 header was always set to a wrong value.
Handle the ipv6 fragments in the same way as the ipv4 ones: keep the first
one and drop the others.
The goal is to have a (roughly) idea about how many packets nDPI needs
to properly classify a flow.
Log this information (and guessed flows number too) during unit tests,
to keep track of improvements/regressions across commits.
