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sica-fondt/knowledge/cyber-skills/skills/detecting-business-email-compromise-with-ai/references/standards.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
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
2026-06-10 06:53:01 +00:00

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Standards & References: Detecting BEC with AI

MITRE ATT&CK References

  • T1566.001/002: Phishing (Spearphishing Attachment/Link)
  • T1534: Internal Spearphishing
  • T1656: Impersonation
  • T1586.002: Compromise Accounts: Email Accounts
  • T1114.003: Email Collection: Email Forwarding Rule

AI/ML Techniques for BEC Detection

Technique Application Accuracy
BERT embeddings + SVC Email classification 98.65%
Transformer NLP Writing style analysis 96%+
Anomaly detection Behavioral baseline deviation 94%+
Graph neural networks Communication pattern analysis 93%+
Sentiment analysis Urgency/manipulation detection 91%+

FBI IC3 BEC Statistics

  • $2.9 billion losses reported in 2023
  • BEC accounts for 27% of all cybercrime financial losses
  • Average loss per BEC incident: $125,000
  • 21,832 BEC complaints filed in 2023

Detection Categories

  • Impostor Detection: AI identifies display name/domain impersonation
  • Account Takeover Detection: Behavioral anomalies from compromised accounts
  • Writing Style Analysis: NLP compares email to sender's historical style
  • Intent Classification: ML classifies email as payment/credential/data request
  • Relationship Analysis: Graph analysis of sender-recipient communication patterns