mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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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
1.4 KiB
1.4 KiB
Workflows: Detecting BEC with AI
Workflow 1: AI-Powered BEC Detection Pipeline
Inbound email arrives
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v
[Feature extraction]
+-- Sender metadata (domain, IP, authentication)
+-- Email content (subject, body, NLP features)
+-- Behavioral context (communication history, timing)
+-- Relationship graph (sender-recipient pattern)
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v
[Multi-model analysis (parallel)]
+-- Impostor classifier: Display name/domain impersonation
+-- NLP model: Writing style vs. sender baseline
+-- Behavioral model: Request anomaly detection
+-- Intent classifier: Payment/credential/data request
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v
[Confidence scoring]
+-- Aggregate model outputs
+-- Weight by model confidence and context
+-- Generate overall BEC probability score
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v
[Action]
+-- Score >= 90%: Auto-quarantine + SOC alert
+-- Score 70-89%: Warning banner + analyst queue
+-- Score 50-69%: Warning banner only
+-- Score < 50%: Deliver normally
Workflow 2: Model Feedback Loop
BEC verdict generated
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v
[User/analyst feedback]
+-- User reports false positive (legitimate email flagged)
+-- Analyst confirms true positive (BEC caught)
+-- User reports missed BEC (false negative)
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v
[Feedback integration]
+-- Update sender trust score
+-- Retrain model with corrected labels
+-- Adjust confidence thresholds
+-- Update behavioral baselines