yarobot 0.2.2

YARA generator inspired by yarGen

yarobot

yarobot is a high-performance YARA rule generator inspired by yarGen, designed to automatically create quality YARA rules from malware samples while minimizing false positives through intelligent goodware database comparison.

🚀 Features

• Automated YARA Rule Generation: Create both simple and super rules from malware samples
• Intelligent Scoring System: Advanced string scoring with goodware database comparison
• High Performance: Core engine written in Rust for maximum speed

🛠 Installation

Install from PyPI

pip install yarobot

Build Prerequisites

• Python 3.11 or higher
• Rust toolchain (for building the native extension)

Install from Source

git clone https://github.com/ogre2007/yarobot
cd yarobot
pip install .

📖 Quick Start

1. Update Goodware Databases

yarobot update-remote # from yarGen project. Doenst work yet. Just go to ex. 3

2. Generate Rules from Malware Samples

py -m yarobot.generate /path/to/malware/samples --output-rule-file my_rules.yar

3. Create Custom Goodware Database

py -m yarobot.database create /path/to/goodware/files --recursive

🎯 Usage Examples

Basic Rule Generation

py -m yarobot.generate /malware/samples \
  --min-size 8 \
  --max-size 128 \
  --min-score 5 \
  --output-rule-file detection_rules.yar

Start as service

server-side:

py -m yarobot.service

client-side:

curl -X POST -F "files=@tests\\data\\binary" http://localhost:5000/api/analyze -F "min_score=5" -F "get_opcodes=true"

Advanced Configuration

py -m yarobot.generate /malware/samples \
  --opcodes \
  --recursive \
  --author "My Security Team" \
  --ref "Internal Investigation 2024" \
  --superrule-overlap 5 \
  --strings-per-rule 15

Database Management

# Update existing database with new goodware samples
(TODO) py -m yarobot.database update /path/to/new/goodware --identifier corporate 

# Create new database from scratch
py -m yarobot.database create /path/to/goodware --opcodes

🔧 Configuration Options

Rule Generation Options

• --min-size, --max-size: String length boundaries
• --min-score: Minimum string score threshold
• --opcodes: Enable opcode feature for additional detection capabilities
• --superrule-overlap: Minimum overlapping strings for super rule creation
• --recursive: Scan directories recursively
• --excludegood: Force exclusion of all goodware strings
• --oe: only executable extensions

Database Options

• --identifier: Database identifier for multi-environment support
• --update: Update existing databases with new samples
• --only-executable: Only process executable file extensions

🏗 Architecture

yarobot combines the performance of Rust with the flexibility of Python:

Core Components

• Rust Engine (yarobot-rs): High-performance file processing and string analysis
• Python Interface: CLI management, database operations, and rule formatting
• Scoring Engine: Intelligent string scoring with goodware comparison
• Rule Generator: YARA rule synthesis and optimization

Database Structure

• good-strings.db: Common strings from goodware samples
• good-opcodes.db: Opcode frequency database
• good-imphashes.db: Import hash database
• good-exports.db: Export function database

🤝 Contributing

We welcome contributions!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

TODO

• dropzone mode
• http-service
• fix/drop imphash/exports
• default databases
• rule generation rewriting
• tokenizer code separated in different package
• dex opcode extraction

📄 License

This project is licensed under the GPLv3 License - see the LICENSE file for details.

🙏 Acknowledgments

• Based on yarGen by Florian Roth
• Built with Pyo3 for Python-Rust integration
• Uses goblin for binary parsing

📞 Support

• Issues: GitHub Issues