code/src/mapper/README.md

# CVE-to-TTP Mapper Module

This module implements CVE-to-TTP (Tactics, Techniques, and Procedures) mapping and inference using two complementary approaches:

1. **CWE-based Mapping**: Uses the existing CWE→CAPEC→TTP mappings generated by the data collection pipeline
2. **TIE Inference**: Uses the [Technique Inference Engine](https://github.com/center-for-threat-informed-defense/technique-inference-engine)  to infer TTPs from CVE descriptions

## Prerequisites

1. **Run the data collection pipeline first** to generate CWE-CAPEC-MITRE mappings:
   ```bash
   python -m src.collectors.main_collector --download
   python -m src.collectors.main_collector --convert
   ```

2. **Install NumPy** (optional, for TIE inference):
   ```bash
   pip install numpy
   ```

3. **Download MITRE ATT&CK data** (required for TIE inference):
   ```bash
   # Run the setup script to download missing data
   python src/mapper/setup_mapper.py
   
   # Or download manually:
   wget https://raw.githubusercontent.com/mitre-attack/attack-stix-data/master/enterprise-attack/enterprise-attack.json -O src/mapper/tie_models/enterprise-attack.json
   ```

## Usage

### 1. As a Python Module

```python
from src.mapper.cve_ttp_mapper import CVEtoTTPMapper
from src.mapper.mapper_config import MapperConfig

# Initialize mapper
config = MapperConfig()
mapper = CVEtoTTPMapper(config)

# Map a single CVE
cve_data = {
    "id": "CVE-2021-44228",
    "description": "Apache Log4j2 JNDI injection vulnerability...",
    "cwe_ids": ["CWE-502", "CWE-20"]
}

result = mapper.map_cve_to_ttps(cve_data)
print(f"Found TTPs: {result['ttps']}")
```

### 2. Command-Line Interface

```bash
# Map a single CVE
python -m src.mapper.mapper_cli single CVE-2021-44228 \
    --description "Remote code execution vulnerability" \
    --cwe CWE-502 --cwe CWE-20

# Process a CVE file
python -m src.mapper.mapper_cli file path/to/cve_data.json \
    --output path/to/mappings.json

# Batch process multiple files
python -m src.mapper.mapper_cli batch path/to/cve_directory \
    --pattern "*.json" \
    --output path/to/output_dir
```

### 3. Test the Module

```bash
# Run the test suite
python src/mapper/test_simple_mapper.py
```

### 4. Batch Processing

```bash
# Process CVE data and generate mappings
python src/mapper/run_cve_ttp_mapping.py
```

## How It Works

### CWE-based Mapping (Direct)
1. Extracts CWE IDs from CVE data
2. Uses pre-computed CWE→CAPEC→TTP mappings
3. Returns all TTPs associated with the CVE's weaknesses

### TIE Inference (ML-based)
1. Extracts keywords from CVE description that indicate techniques
2. Uses pre-trained TIE model to infer related techniques
3. Returns predicted TTPs with confidence scores

### Combined Approach
The mapper combines both methods:
- First applies CWE-based mapping for direct associations
- Then uses TIE to infer additional TTPs from the description
- Deduplicates and returns comprehensive results

## Output Format

```json
{
  "cve_id": "CVE-2021-44228",
  "ttps": ["T1190", "T1059", "T1203"],
  "methods_used": ["CWE-to-CAPEC-to-TTP", "TIE-inference"],
  "total_ttps_found": 3,
  "details": {
    "cwe_mapping": {
      "method": "CWE-to-CAPEC-to-TTP",
      "mappings": [
        {
          "cwe": "CWE-502",
          "capecs": ["CAPEC-586"],
          "ttps": ["T1203"]
        }
      ]
    },
    "tie_inference": {
      "method": "tie_inference",
      "keyword_ttps": ["T1190"],
      "inferred_ttps": ["T1059"],
      "confidence_scores": {
        "T1059": 0.85
      }
    }
  },
  "mapping_timestamp": "2024-01-01T12:00:00"
}
```

## Configuration

Edit `mapper_config.py` to customize:
- TIE confidence threshold
- Maximum predictions per CVE
- Input/output paths