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27f6252 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # 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
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