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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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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
37 lines
1.5 KiB
Markdown
37 lines
1.5 KiB
Markdown
# API Reference — Performing False Positive Reduction in SIEM
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## Libraries Used
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- **csv**: Parse SIEM alert export files (Splunk, QRadar, Sentinel)
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- **collections.Counter**: Aggregate alert patterns by rule, source, severity
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## CLI Interface
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```
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python agent.py analyze --csv alerts.csv [--threshold 5]
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python agent.py tune --csv alerts.csv
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python agent.py simulate --csv alerts.csv [--disable-rules "Rule A" "Rule B"] [--whitelist-sources 10.0.0.1]
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```
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## Core Functions
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### `analyze_alerts(csv_file, threshold)` — Identify false positive patterns
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Parses alert CSV, calculates per-rule FP rates, identifies noisy rules exceeding threshold.
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Returns: total alerts, FP count/rate, noisy rules ranked by FP rate, top FP sources.
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### `generate_tuning_recommendations(csv_file)` — Create tuning action plan
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Maps FP rates to actions: DISABLE (>=90%), ADD_WHITELIST (>=70%), TUNE_THRESHOLD (>=50%), REVIEW (<50%).
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### `simulate_tuning_impact(csv_file, rules_to_disable, sources_to_whitelist)` — Model tuning changes
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Calculates alert volume reduction and new FP rate after applying proposed rule disables and source whitelists.
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## Expected CSV Columns
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- `rule_name` / `Rule` / `alert_name`: Detection rule identifier
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- `src_ip` / `source_ip` / `Source`: Source IP address
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- `status` / `Status` / `disposition`: Alert disposition (false_positive, fp, closed_fp, benign)
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- `severity` / `Severity`: Alert severity level
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## FP Status Keywords
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`false_positive`, `fp`, `closed_fp`, `benign`
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## Dependencies
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No external packages — Python standard library only.
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