Files
DECNET/tests/fixtures/campaigns/noise_floor.expected.yaml
anti 75af00c9c8 test(clustering): full-bound passes through production campaign clusterer
Runs the chained identity + campaign clustering pipeline against all
seven fixtures via from_synthetic / from_synthetic_identity adapters
and ratchets every YAML floor to 1.0 — the production clusterer
(and the reference clusterers used in the per-fixture tests) all
score perfectly across ARI / homogeneity / completeness /
singleton_recall on each fixture.

Three substrate fixes surfaced by the ratchet:

- Tuning: shared_infra now Jaccards payload+C2 only; decky_set moved
  into cohort_weight to prevent fleet-scarcity false-merges (F1's
  shared_wordlist failure mode). Tier weight raised to 1.0 so
  shared payload+C2 alone crosses threshold (F5's intended pass).
- Adapter: from_synthetic_identity now reads SyntheticSession
  started_at + duration_s for session_windows and per-decky
  timestamps (the production-row adapter still uses start_ts/end_ts
  when available).
- Fixture data: paused_campaign.yaml's JA3 collided exactly with
  vpn_hopping.yaml's (same TLS extension list). The collision
  fused two unrelated campaigns under the chained identity layer
  in the noise_floor composite. Made paused's JA3 distinct.

Also wires Campaign / CampaignsResponse into models/__init__.py's
__all__ that was missed in the schema commit.
2026-04-26 09:13:59 -04:00

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YAML

# Bounds for fixture 6 (noise_floor).
#
# Composite corpus: ~14 campaign-driven attackers (across 5 prior
# fixtures' actors / rotations) + 18 truth-singleton noise rows
# (8 inherited from lone_wolf + 10 added by this fixture).
#
# A correct algorithm groups every campaign correctly and leaves
# every singleton singleton — score 1.0 across every metric.
#
# Singleton recall is the load-bearing metric here: noise
# absorption is the failure mode that makes campaign attribution
# useless in practice (a clusterer that pulls noise into real
# campaigns dilutes attribution to nothing). The bound floor on
# singleton_recall is what would catch that regression.
#
# Bounds are loose at v1; tighten as the algorithm matures.
adjusted_rand_index:
min: 1.0
homogeneity:
min: 1.0
completeness:
min: 1.0
singleton_recall:
min: 1.0