marc-shade/fraud-detection-mcp

Эта страница переведена автоматически. Технические детали смотрите в оригинале. Открыть оригинал

6 stars 3 forks Качество 71 Тренд 14MIT

Fraud detection MCP server: Isolation Forest + Autoencoder ensemble, behavioral biometrics, agent-to-agent transaction protection, defense compliance (NITTF/SIEM/SEAD). 24 tools, 831 tests.

Обзор

Part of the Agentic System - a 24/7 autonomous AI framework with persistent memory. - : Anomaly detection for real-time transaction screening - : PyTorch-based deep learning anomaly detection using reconstruction error scoring. Ensemble with Isolation Forest (configurable 60/40 weighting). - : Keystroke dynamics (Isolation Forest), mouse movement patterns (One-Class SVM), touch screen patterns (LOF) - : NetworkX-based graph centrality metrics (degree, clustering coefficient, betweenness, closeness) for fraud ring detection - : Comprehensive feature engineering with cyclical encoding, z-scores, and velocity features - : Automatic detection of AI agent vs human traffic (Stripe ACP, Visa TAP, Mastercard Agent Pay, Google AP2, PayPal, Coinbase, OpenAI, Anthropic, x402) - : API key format validation, JWT token expiry checks, JSON-backed agent registry - : Per-agent Isolation Forest baselines (8 features, max 1000 observations/agent) - : Spending limits, merchant whitelists/blocklists,...

README

Показан фрагмент оригинального README. Открыть оригинал.

Advanced Fraud Detection MCP

Fraud detection and anomaly analysis for financial security — with AI agent-to-agent transaction protection.

Part of the Agentic System - a 24/7 autonomous AI framework with persistent memory.

Overview

An open-source Model Context Protocol (MCP) server for fraud detection using machine learning and behavioral analysis. This system combines behavioral biometrics, anomaly detection, network graph analysis, and AI agent behavioral fingerprinting for fraud prevention.

Key Features

Core Detection (Active by Default)

Isolation Forest: Anomaly detection for real-time transaction screening
Autoencoder Ensemble: PyTorch-based deep learning anomaly detection using reconstruction error scoring. Ensemble with Isolation Forest (configurable 60/40 weighting).
Behavioral Biometrics: Keystroke dynamics (Isolation Forest), mouse movement patterns (One-Class SVM), touch screen patterns (LOF)
Network Graph Analysis: NetworkX-based graph centrality metrics (degree, clustering coefficient, betweenness, closeness) for fraud ring detection
46-Feature Pipeline: Comprehensive feature engineering with cyclical encoding, z-scores, and velocity features

Agent-to-Agent Transaction Protection

Traffic Classification: Automatic detection of AI agent vs human traffic (Stripe ACP, Visa TAP, Mastercard Agent Pay, Google AP2, PayPal, Coinbase, OpenAI, Anthropic, x402)
Agent Identity Verification: API key format validation, JWT token expiry checks, JSON-backed agent registry
Agent Behavioral Fingerprinting: Per-agent Isolation Forest baselines (8 features, max 1000 observations/agent)
Mandate Compliance: Spending limits, merchant whitelists/blocklists, time windows, geographic restrictions
Collusion Detection: Directed graph analysis for circular flows, temporal clustering, and volume anomalies
Agent Reputation Scoring: Longitudinal trust from history, consistency, and collusion safety (weighted 40/25/25/10)

Defense Insider Threat Compliance

28 Behavioral Indicators from the NITTF Insider Threat Guide (EO 13587)
SIEM Integration: CEF (ArcSight), LEEF (QRadar), Syslog RFC 5424 with MITRE ATT&CK enrichment
Cleared Personnel Monitoring: SEAD 4/6 continuous evaluation, 13 adjudicative guidelines

Открыть полный README на GitHub

Установка

This server does not publish a one-line install command.

Open the repository installation guide Новичок в MCP? Начните с руководства на mcpskill.org →

Конфигурация

{
  "mcpServers": {
    "fraud-detection-mcp": {
      "command": "/path/to/fraud-detection-mcp/venv/bin/python",
      "args": ["/path/to/fraud-detection-mcp/server.py"],
      "env": {
        "FRAUD_DETECT_MODEL_PATH": "/path/to/fraud-detection-mcp/models",
        "FRAUD_DETECT_LOG_LEVEL": "INFO"
      }
    }
  }
}