# Global Payments UHG Use Cases
## Overview
This document identifies cybersecurity use cases where Universal Hyperbolic Geometry (UHG) and Hyperbolic Graph Neural Networks provide unique advantages for Global Payments' Splunk SOAR implementation.

## Current Splunk ML Coverage
- DNS Exfiltration Detection
- Suspicious DNS TXT Record Detection
- Suspicious Process Name Detection
- DGA Model (Pretrained)

## High-Value UHG Use Cases

### 1. Payment Network Topology Analysis
**Why UHG is Needed:**
- Payment networks have inherent hierarchical structure (merchants → processors → banks → card networks)
- Transaction paths exhibit tree-like branching with exponential growth
- Hyperbolic space naturally models hierarchical financial relationships

**Specific Applications:**
- Detect anomalous transaction routing patterns
- Identify merchant compromise patterns
- Map payment network topology changes
- Track transaction path deviations

### 2. Cross-Channel Fraud Detection
**Why UHG is Needed:**
- Fraud patterns span multiple channels (in-store, online, mobile)
- Channel relationships form complex hierarchies
- Traditional Euclidean models struggle with multi-channel patterns

**Specific Applications:**
- Model cross-channel transaction sequences
- Detect channel-hopping fraud patterns
- Identify compromised channel combinations
- Track fraud pattern evolution across channels

### 3. Merchant Category Hierarchy Analysis
**Why UHG is Needed:**
- Merchant categories form natural hierarchical trees
- Category relationships have inherent power-law distributions
- Fraud patterns often exploit category relationships

**Specific Applications:**
- Detect anomalous merchant category transitions
- Identify category-based fraud patterns
- Track merchant category drift
- Map category relationship changes

### 4. Authorization Network Analysis
**Why UHG is Needed:**
- Authorization networks have deep hierarchical structure
- Message routing forms complex trees
- Network relationships exhibit power-law scaling

**Specific Applications:**
- Detect authorization path anomalies
- Identify authorization network attacks
- Track authorization pattern changes
- Map authorization relationship evolution

### 5. Terminal Network Security
**Why UHG is Needed:**
- POS terminal networks form hierarchical structures
- Terminal relationships exhibit scale-free properties
- Attack patterns follow network hierarchy

**Specific Applications:**
- Detect terminal compromise patterns
- Identify terminal network attacks
- Track terminal behavior changes
- Map terminal relationship evolution

## Implementation Priority

1. Payment Network Topology Analysis
   - Highest impact on fraud detection
   - Clear hierarchical structure
   - Direct financial impact

2. Cross-Channel Fraud Detection
   - Growing threat vector
   - Complex relationship patterns
   - High business value

3. Authorization Network Analysis
   - Critical infrastructure protection
   - Clear network structure
   - High security impact

4. Merchant Category Analysis
   - Supporting analysis capability
   - Clear hierarchical structure
   - Fraud pattern insights

5. Terminal Network Security
   - Infrastructure protection
   - Complex network patterns
   - Operational security impact

## Technical Requirements

### UHG Library Extensions Needed:
1. Payment Network Layers
   - Hierarchical payment graph layers
   - Transaction path analysis
   - Network topology mapping

2. Cross-Channel Analysis
   - Channel relationship layers
   - Pattern sequence analysis
   - Channel transition mapping

3. Authorization Analysis
   - Message routing layers
   - Path analysis capabilities
   - Network pattern detection

4. Category Analysis
   - Hierarchical category layers
   - Category relationship mapping
   - Transition pattern detection

5. Terminal Analysis
   - Terminal network layers
   - Behavior pattern analysis
   - Network topology mapping

### Splunk SOAR Integration:
1. Data Pipeline Requirements
   - Real-time transaction feeds
   - Authorization message streams
   - Terminal network data
   - Merchant category updates

2. Model Deployment
   - PyTorch model serving
   - Real-time inference
   - Batch analysis capabilities

3. Alert Integration
   - Custom alert actions
   - Pattern detection triggers
   - Anomaly notifications

## Next Steps

1. Prototype Development
   - Select highest priority use case
   - Develop proof of concept
   - Test with sample data

2. UHG Library Extensions
   - Implement required layers
   - Add network analysis capabilities
   - Create integration utilities

3. Splunk Integration
   - Develop data pipelines
   - Create model deployment framework
   - Implement alert actions

4. Validation and Testing
   - Test with production data
   - Validate detection capabilities
   - Measure performance impact 