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.
Multi-month APT campaign modeling real APT operational tempo: recon
over weeks, exploitation later, action-on-objectives later still.
The unique signal this fixture stresses is TIME-AGNOSTIC IDENTITY
across multi-week silences — a clusterer that silently expires old
edges fragments any campaign that operates over months.
Three DSL actors represent the operator's three operational windows
(week 2, month 2, month 3 of a 90-day campaign), all sharing JA3 +
HASSH + payload + C2 callback. Campaign-level fixture only — the
three actors mint distinct truth_identity_id rows by design (same
modeling caveat as fixtures 4 and 5).
The fixture's narrative mirrors how an APT works a deep nested
topology (DECNET MazeNET mode): map decoy networks for weeks, only
then commit to exploitation. Slow-and-low pacing is the signal.
recency_decay_clusterer added to fixture_harness — same edge
construction as composite_signals_clusterer, but each edge weighted
by exp(-time_distance / half_life_days) and dropped below a
threshold. Adversarial reference for slow_burn: with 14-day half-
life and 0.5 threshold, edges between operational windows (24+ days
apart) decay below threshold and drop. The campaign fragments into
three clusters; completeness collapses.
This is the canonical production failure mode for graph clusterers
that bound memory or bias toward "what's hot" by silently expiring
old edges. Catching it in synthetic data is what fixture 7 exists
for; the replay tier will surface real-world drift / dwell patterns
that calibrate the half-life threshold the real algorithm should
tolerate.
Four tests: corpus shape (window-isolated sessions, stable
fingerprint), pipeline pass via composite_signals_clusterer (time-
agnostic — folds all three windows), adversarial fragmentation
(3 clusters at 14-day half-life), long-half-life sanity (gentle
decay unions everything; confirms behavior depends on the half-life
parameter, not on something unrelated).
Bundles all five prior fixtures' campaigns into one corpus alongside
10 fresh Delivery-only noise scanners (on top of lone_wolf's 8
inherited). The fixture covers cross-corpus interference — signal
collisions across fixtures' JA3/HASSH/C2 strings, factory ID re-use,
clusterer ambiguity that only manifests when multiple campaigns
score together. Each constituent fixture already ships its own
in-fixture adversarial test; this one is the control for the class
of failures that single-corpus fixtures cannot catch.
Composition is declared via a fixture-6-specific include_fixtures
block in noise_floor.yaml. The test file's loader expands it into
a full corpus.campaigns spec at runtime so the factory itself stays
unaware — no factory primitive added for what only this fixture
needs. The 8 noise scanners declared by lone_wolf flow through
naturally; the extra_noise_scanners count adds 10 more.
composite_signals_clusterer (added in the fixture-5 commit) is the
pass clusterer — union-find combining (ja3, hassh) match OR
overlapping C2 callback. Approximates the planned similarity graph
well enough that every campaign resolves and every singleton stays
singleton in the merged corpus.
Three tests: corpus integrity (every campaign id present, 12
campaign-driven attackers + 18 noise = 30 total), pipeline pass
against the global bounds, and an explicit singleton-recall
assertion (21 truth-singletons — 1 lone wolf, 18 noise, 2
shared_wordlist actors whose campaigns are size 1 — all kept
singleton by the composite clusterer). Singleton recall is the
load-bearing metric here: noise absorption is the failure mode
that makes campaign attribution useless in practice.
Three new reference clusterers in fixture_harness:
* c2_callback_clusterer — union-find on overlapping C2 callback
sets across an attacker's sessions. Pass-clusterer for fixture 5
where two operators with distinct tooling share a C2 endpoint as
the campaign signal.
* shift_clusterer — deliberately-bad reference that buckets
attackers by majority session-start hour into night/day/swing.
Adversarial reference for fixture 5; proves operational schedule
is NOT a campaign signal.
* composite_signals_clusterer — union-find combining (ja3, hassh)
match OR overlapping C2 callback. Will serve as the pass-
clusterer for fixture 6 (noise_floor) where multiple campaigns
with heterogeneous signal types are scored together.
Also factored a small _union_find helper for the new clusterers
(existing time_window/credential_jaccard left untouched to avoid
mixing refactor with feature work).
Fixture 5 (multi_operator): one campaign, two operators with
distinct UKC roles. Actor A (broker, night shift): Delivery →
Exploitation → Persistence → C2. Actor B (post-ex, day shift):
Discovery → Lateral Movement → Collection → Exfiltration.
Distinct JA3/HASSH/ASN/IPs; shared C2 + payload hash.
Four tests: corpus shape (distinct fingerprints, shared C2,
disjoint shifts), pipeline pass via c2_callback_clusterer,
explicit harness sanity that fingerprint_clusterer cannot resolve
this fixture (documents which signal carries the campaign), and
adversarial shift_clusterer fragmentation.
Phase-handoff edges (the real load-bearing signal per the design
doc) wait for the production clusterer; this fixture will prove
they're needed when it ships.
Adds the actor.active_days primitive to the campaign factory so a
DSL actor can be bound to specific day indexes. Falls back to the
non-paused day pool when absent (existing fixtures unchanged).
Intersects with pause_windows so the campaign-wide silence still
wins if both are set.
Adds time_window_clusterer reference to fixture_harness — union-find
over attackers, edge if their session time-ranges are within
gap_days of each other. Deliberately-bad reference for fixture 4:
multi-day silent stretches fragment a single campaign because the
clusterer has no signal that bridges the gap.
Fixture 4 (paused_campaign): one campaign modeled as two DSL actors
representing the operator's two operational windows (active days
1-2 and 6-7), separated by a silent stretch (days 3-5). Both share
JA3 + HASSH + payload + C2 callback; only their active_days differ.
Five tests: corpus shape (rows in their windows, shared signals),
pipeline pass via fingerprint_clusterer at level=campaign,
adversarial fragmentation via time_window_clusterer (1-day union
threshold cannot bridge the 4-day silence → completeness collapses),
huge-gap sanity (gap_days=10 unions both halves), silent-stretch
invariant (no session leaks into the configured pause window).
Identity-level scoring is fixture 2's job; this fixture is
campaign-level only — modeling caveat documented in the YAML.
One campaign, one DSL actor, ip_pool: rotating + rotation_count: 5
across 5 synthetic private-use ASNs (RFC 6996 64512-64516). Stable
JA3, HASSH, and payload_hash across every rotation — these are the
"signals the attacker can't cheaply rotate" per IDENTITY_RESOLUTION.md
and the load-bearing reason all 5 observation rows must resolve to
one identity / one campaign.
Two new reference clusterers in fixture_harness.py:
* fingerprint_clusterer — groups by (ja3, hassh). Un-fingerprinted
rows stay singleton so it doesn't trivially fuse all noise into one
mega-cluster. Approximates the stable-signal arm of the planned
similarity graph.
* asn_clusterer — deliberately-bad reference for fixture 2's
adversarial test. Group-by-ASN shatters the campaign into 5
singletons; completeness collapses to 0.
Four tests in test_vpn_hopping_fixture.py: corpus shape (5 rows, 1
identity, 1 campaign, 5 distinct ASNs/IPs, stable fingerprints),
pass at campaign level, pass at identity level (asserts ARI exactly
1.0), asn_clusterer breaches the completeness floor.
Two campaigns sharing a credential wordlist; everything else (ASN, IPs,
JA3, HASSH, active hours) divergent. Pass condition: clusterer must NOT
merge. Protects against the "credential overlap is identity" failure
mode that commodity wordlists invite.
* tests/clustering/fixture_harness.py — shared assert_fixture_bounds
helper + identity_clusterer (placeholder, trivially correct on
all-singleton fixtures) + credential_jaccard_clusterer (deliberately-
bad reference used to PROVE the fixture catches what it should).
* tests/clustering/test_shared_wordlist_fixture.py — bounds pass with
identity, bounds FAIL (homogeneity → 0) with the bad credential
clusterer. The latter is the proof the fixture earns its keep.
* tests/fixtures/campaigns/shared_wordlist.{yaml,expected.yaml}.
* tests/clustering/test_lone_wolf_fixture.py — refactored onto the
shared harness. No behavior change.
Pre-implementation scaffolding for campaign clustering. The simulator is
the spec — algorithm code follows once fixtures + metrics are stable.
* decnet/clustering/ukc.py — UKCPhase enum (19 phases across In/Through/Out
stages), OBSERVABLE_PHASES set, stage_of() helper. Vocabulary aligns
with future MITRE ATT&CK tagging so synthetic data and runtime phase
inference don't need renaming when TTP-tagging lands.
* tests/factories/campaign_factory.py — YAML DSL parser + deterministic
generator emitting truth-labeled SyntheticAttacker / SyntheticSession
records. Validates phase names, warns on unobservable phases, supports
multi-campaign + noise corpora.
* tests/clustering/metrics.py — pure-Python ARI / homogeneity /
completeness / singleton_recall (no sklearn dep). Decided before any
algorithm exists, on purpose.
* tests/fixtures/campaigns/lone_wolf.{yaml,expected.yaml} — fixture 3
from the design doc; simplest of the six, exercises the full pipeline
with an identity-clusterer placeholder.
* development/CAMPAIGN_CLUSTERING.md — design spec for the feature.
* development/DEVELOPMENT_V2.md — note on DSL evolution path
(concurrent phases, multi-actor per phase) deferred post-v1.
