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
31 lines
1.3 KiB
Markdown
31 lines
1.3 KiB
Markdown
# Standards & References: Detecting BEC with AI
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## MITRE ATT&CK References
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- **T1566.001/002**: Phishing (Spearphishing Attachment/Link)
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- **T1534**: Internal Spearphishing
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- **T1656**: Impersonation
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- **T1586.002**: Compromise Accounts: Email Accounts
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- **T1114.003**: Email Collection: Email Forwarding Rule
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## AI/ML Techniques for BEC Detection
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| Technique | Application | Accuracy |
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| BERT embeddings + SVC | Email classification | 98.65% |
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| Transformer NLP | Writing style analysis | 96%+ |
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| Anomaly detection | Behavioral baseline deviation | 94%+ |
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| Graph neural networks | Communication pattern analysis | 93%+ |
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| Sentiment analysis | Urgency/manipulation detection | 91%+ |
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## FBI IC3 BEC Statistics
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- $2.9 billion losses reported in 2023
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- BEC accounts for 27% of all cybercrime financial losses
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- Average loss per BEC incident: $125,000
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- 21,832 BEC complaints filed in 2023
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## Detection Categories
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- **Impostor Detection**: AI identifies display name/domain impersonation
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- **Account Takeover Detection**: Behavioral anomalies from compromised accounts
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- **Writing Style Analysis**: NLP compares email to sender's historical style
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- **Intent Classification**: ML classifies email as payment/credential/data request
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- **Relationship Analysis**: Graph analysis of sender-recipient communication patterns
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