📢 This PR is a bit bigger, so commit-by-commit review might be easier. I've tried to make the commit comments as informative as possible.

Main changes

• Implement both the standard and the type-specific endpoint filters as EndpointCharacteristics.
• Move the definitions of isEffectiveSink and getAReasonSinkExcluded to the base class, as they can now be implemented generically for all sink types.
• Changing the definition of getAReasonSinkExcluded is how we'd adjust which endpoints we score at inference time. For now I've implemented it to replicate the logic in the old code, so that results remain unaffected. I've tracked possible experiments to improve this selection in github/ml-ql-adaptive-threat-modeling#2126.

A few notes

Note that this PR still sticks to the principle of not breaking any tests, except that I had to disambiguate three filters from three different sink types that all had the same name (fc56c5a), and that required a tiny update to FilteredTruePositives.expected (0fd013f).

Also note that the training data is unaffected because (for now) I've given all EndpointFilterCharacteristics medium confidence, whereas only high-confidence characteristics contribute to training set selection. AIUI, the reason endpoint filters weren't used to select negative training samples in the old code was precisely this: their accuracy is high enough that we don't want to waste inference time scoring these endpoints, but not high enough that we can reliably use them as negative training samples. It's worth having someone with the needed expertise (Stephan? 😉) go through them eventually to consider whether any should be promoted to high confidence. I tracked this possible experiment in github/ml-ql-adaptive-threat-modeling#2126.

Timing checks

• ✅ KPI timing experiment: github/codeql-dca-main#8634
• ☑️ The local runtime of endpoint_large_scale/ExtractEndpointDataTraining remains like it was after the last PR: About 5s.

Closes github/ml-ql-adaptive-threat-modeling#2100

Possibly also closes github/ml-ql-adaptive-threat-modeling#2101?