Engineer builds fraud detection pen

Short summary

Engineer created a fraud detection penetration testing tool using Random Forest to simulate evasion attempts and identify detection gaps. Demonstrates ML ops principles including training/serving separation and security hardening (input validation, CSRF protection, rate limiting, structured logging). Deployable as Flask backend or standalone GitHub Pages educational frontend.

• •Fraud detection penetration testing tool using Random Forest ML model for authorized security assessment
• •Demonstrates ML ops patterns and comprehensive security hardening across validation, CSRF, rate limiting, and logging
• •Dual deployment: production-grade Flask backend and public-facing educational GitHub Pages frontend

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