mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2.1 KiB
2.1 KiB
SIEM Use Case Tuning API Reference
Splunk Notable Event Export
Export Notables via SPL
| inputlookup notable_events
| search status_label IN ("New", "In Progress", "Resolved")
| table rule_name, _time, status_label, src, dest, user, urgency
| rename status_label as disposition, _time as timestamp
| outputlookup alert_export.csv
Splunk ES Correlation Search Tuning
# Measure FP rate per correlation search over 30 days
| inputlookup notable_events where earliest=-30d
| eval is_fp=if(status_label="Resolved" AND disposition="False Positive", 1, 0)
| stats count as total, sum(is_fp) as fp_count by rule_name
| eval fp_rate=round(fp_count/total, 4)
| sort -fp_rate
Update Correlation Search Threshold
POST /servicesNS/nobody/SplunkEnterpriseSecuritySuite/saved/searches/{search_name}
Content-Type: application/x-www-form-urlencoded
search=<updated_spl_with_new_threshold>
Elastic Detection Rule Tuning
List Detection Rules
GET /_security/detection_engine/rules/_find?per_page=100
Authorization: ApiKey <base64_api_key>
Add Exception to Rule
POST /_security/detection_engine/rules/exceptions
{
"rule_id": "rule-uuid",
"name": "Whitelist scanner IPs",
"entries": [
{
"field": "source.ip",
"operator": "is_one_of",
"value": ["10.0.1.50", "10.0.1.51"],
"type": "match_any"
}
]
}
Query Rule Execution Stats (Kibana)
event.kind: "signal" AND kibana.alert.rule.name: "Brute Force Detection"
| stats count by kibana.alert.workflow_status
Alert Tuning Metrics
| Metric | Formula | Target |
|---|---|---|
| False Positive Rate | FP / (FP + TP) | < 30% |
| Precision | TP / (TP + FP) | > 70% |
| Alert-to-Incident Ratio | Incidents / Total Alerts | > 20% |
| Mean Time to Triage | avg(triage_end - alert_time) | < 15 min |
CLI Usage
# Analyze alert CSV export
python agent.py --alert-csv notable_export.csv --output tuning.json
# Adjust FP threshold for whitelist candidates
python agent.py --alert-csv alerts.csv --fp-threshold 0.9 --top-rules 10
# CSV format: rule_name,timestamp,disposition,source,user,severity