- docker-compose.submissions.yml: rename submissions-contrib1/contrib2
to submissions-contrib1/contrib2; update paths and host env vars
- podman-standalone.sh: replace 'Contributor's instance' with generic
'WireGuard-connected Docker hosts'
- docker-standalone.sh: replace personal node-id in harvest endpoint
- Rename patterns/sources-example-node.yaml → patterns/sources-example.yaml
and update header/comments to be host-agnostic
- Replace internal node names in gen_corpus.py _HOSTS with generic names
- Replace example-node hostname in syslog test fixtures with testhost
- Replace example-node example in mcp_server.py doc with myserver
- Replace private LAN IP (<YOUR_HOST_IP>) in docker-standalone.sh with
<HEIMDALL_LAN_IP> placeholder
- Replace private IPs in sources-cluster.yaml comments with <YOUR_HOST_IP>
- Remove instance-specific hostname from llm.py fallback comment
- Replace Caddy example domain in podman-standalone.sh with placeholder
Second-pass cybersec classifier using DeBERTa-v3-base-mnli (already
cached — no download required). Runs after each anomaly scoring pass on
entries flagged by the anomaly scorer or with pattern matches.
Architecture:
- app/services/cybersec.py: zero-shot-classification pipeline with 5
cybersec candidate labels (auth failure, privilege escalation, network
intrusion, malware, data exfiltration). Writes ml_score/ml_label/
ml_scored_at to log_entries; inserts high-confidence hits into
detections with scorer='cybersec'.
- app/tasks/cybersec_scorer.py: async background task (same shape as
anomaly_scorer.py).
- REST: GET/POST /turnstone/api/cybersec/status|run|detections.
GET /turnstone/api/anomaly/detections now accepts scorer= filter.
Schema: ml_score, ml_label, ml_scored_at added to log_entries; scorer
column added to detections (idempotent migrations + DDL for both SQLite
and Postgres).
UI: Security Alerts view gains Source dropdown (All / Anomaly / Cybersec)
and cybersec scorer status badge. Label dropdown split into optgroups.
Deployment: TURNSTONE_CYBERSEC_MODEL/DEVICE/THRESHOLD vars added to
.env.example, docker-compose.yml, docker-standalone.sh.
Tests: 10 new tests — no model, no eligible entries, scoring, detection
creation, normal label suppression, threshold filtering, pattern-tag
filtering, idempotency, list filtering, scorer column filter.
416/416 passing.
Closes: #9
Add TURNSTONE_ANOMALY_* env vars to docker-compose.yml, docker-standalone.sh,
and .env.example. Mount shared HF model cache (/Library/Assets/LLM on Heimdall)
as read-only bind in both compose and standalone — avoids re-downloading models
that are already cached by the diagnose pipeline.
Heimdall: byviz/bylastic_classification_logs already cached, threshold 0.80,
glean-triggered only (TURNSTONE_ANOMALY_INTERVAL=0).
Contributor2's example-node.tv has no WireGuard route to Heimdall's LAN (10.1.10.x),
so the <YOUR_HOST_IP>:7700 private address is unreachable from there.
Use the public cf-orch endpoint instead:
GPU_SERVER_URL=https://orch.circuitforge.tech
Contributor's Huginn has WireGuard to Heimdall LAN — <YOUR_HOST_IP>:7700 stays correct.
Added both options to docker-standalone.sh for clarity.
