Anthropic-Cybersecurity-Skills/skills/detecting-business-email-compromise-with-ai/references/standards.md

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