Camillo Maria Caruso

19 papers Journal 15Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Filippo Ruffini, Camillo Maria Caruso, Claudia Tacconi, Lorenzo Nibid, Francesca Miccolis, Marta Lovino, Carlo Greco, Edy Ippolito, Michele Fiore, Alessio Cortellini, Bruno Beomonte Zobel, Giuseppe Perrone, Bruno Vincenzi, Claudio Marrocco, Alessandro Bria, Elisa Ficarra, Sara Ramella, Valerio Guarrasi, Paolo Soda
2026 J jnl
CoRR
Alice Natalina Caragliano, Giulia Farina, Fatih Aksu, Camillo Maria Caruso, Claudia Tacconi, Carlo Greco, Lorenzo Nibid, Edy Ippolito, Michele Fiore, Giuseppe Perrone, Sara Ramella, Paolo Soda, Valerio Guarrasi
2026 J jnl
AI Open
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2026 J jnl
CoRR
Daniele Molino, Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2025 J jnl
Image Vis. Comput.
Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso, Francesco Di Feola, Aurora Rofena, Filippo Ruffini, Paolo Soda
2025 J jnl
CoRR
Afifa Khaled, Mohammed Sabir, Rizwan Qureshi, Camillo Maria Caruso, Valerio Guarrasi, Suncheng Xiang, S. Kevin Zhou
2025 J jnl
Comput. Biol. Medicine
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2025 conf
ICIAP (Workshops 1)
Camillo Maria Caruso, Riccardo Bruni, Valerio Guarrasi
2025 conf
Ital-IA
Rosa Sicilia, Fatih Aksu, Alessandro Bria, Alice Natalina Caragliano, Camillo Maria Caruso, Ermanno Cordelli, Arianna Francesconi, Valerio Guarrasi, Giulio Iannello, Guido Manni, Massimiliano Mantegna, Giustino Marino, Daniele Molino, Elena Mulero Ayllon, Filippo Ruffini, Linlin Shen, Matteo Tortora, Paolo Soda
2025 J jnl
CoRR
Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, Valerio Guarrasi
2024 J jnl
CoRR
Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso, Francesco Di Feola, Aurora Rofena, Filippo Ruffini, Paolo Soda
2024 J jnl
Comput. Methods Programs Biomed.
Camillo Maria Caruso, Valerio Guarrasi, Sara Ramella, Paolo Soda
2024 J jnl
CoRR
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2024 J jnl
CoRR
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
2024 conf
Ital-IA
Fatih Aksu, Alessandro Bria, Alice Natalina Caragliano, Camillo Maria Caruso, Wenting Chen, Ermanno Cordelli, Omar Coser, Arianna Francesconi, Leonardo Furia, Valerio Guarrasi, Giulio Iannello, Clemente Lauretti, Guido Manni, Giustino Marino, Domenico Paolo, Filippo Ruffini, Linlin Shen, Rosa Sicilia, Paolo Soda, Christian Tamantini, Matteo Tortora, Zhuoru Wu, Loredana Zollo
2023 J jnl
IEEE Access
Camillo Maria Caruso, Paolo Soda, Carlo Giammichele, Francesca Rotilio, Rosa Sicilia
2023 J jnl
CoRR
Camillo Maria Caruso, Valerio Guarrasi, Sara Ramella, Paolo Soda
2023 conf
Ital-IA
Valerio Guarrasi, Lorenzo Tronchin, Camillo Maria Caruso, Aurora Rofena, Guido Manni, Fatih Aksu, Domenico Paolo, Giulio Iannello, Rosa Sicilia, Ermanno Cordelli, Paolo Soda
2022 J jnl
J. Imaging
Camillo Maria Caruso, Valerio Guarrasi, Ermanno Cordelli, Rosa Sicilia, Silvia Gentile, Laura Messina, Michele Fiore, Claudia L. Piccolo, Bruno Beomonte Zobel, Giulio Iannello, Sara Ramella, Paolo Soda
docs/macho_extractors_README.md
← Index docs/macho_extractors_README.md markdown
# MachO Extractors for RedB

This document describes the MachO extractors implementation for the RedB binary analysis framework.

## Overview

The MachO extractors provide comprehensive analysis capabilities for Mach-O binaries (macOS, iOS, watchOS, tvOS executables) following the same pattern as the existing PE extractors. The implementation uses the `machofile` library located in the `docs/` folder.

## Architecture

### Main Components

1. **MachOExtractor** (`redb/extractors/macho_extractor.py`)
   - Abstract base class for all MachO extractors
   - Handles MachO file parsing and common functionality
   - Supports both single-architecture and Universal/FAT binaries

2. **Individual Extractors** (`redb/extractors/macho_extractors/`)
   - `macho_features.py` - Basic MachO header and metadata
   - `macho_segments.py` - Segment information and analysis
   - `macho_imports.py` - Imported functions and libraries
   - `macho_exports.py` - Exported symbols
   - `macho_dylibs.py` - Dynamic library dependencies
   - `macho_signature.py` - Code signing information

3. **Data Models** (`redb/models/dataclasses.py`)
   - MachO-specific dataclasses for structured data storage
   - Compatible with Elasticsearch and ClickHouse exporters

## Features

### Supported Binary Types
- Single-architecture Mach-O binaries (32-bit and 64-bit)
- Universal/FAT binaries with multiple architectures
- All major CPU architectures (x86, x86_64, ARM, ARM64)

### Extracted Information

#### MachO Features
- Header information (magic, CPU type, file type, flags)
- Architecture detection
- Entry point information
- UUID
- Version information
- Signing status
- Encryption status
- Counts (segments, dylibs, imports, exports)

#### Segments
- Segment names and properties
- Virtual addresses and sizes
- File offsets and sizes
- Protection flags
- Entropy calculation
- Segment hashes (MD5, SHA256)

#### Imports
- Imported function names
- Library dependencies
- Import counts and statistics

#### Exports
- Exported symbol names
- Export counts and statistics

#### Dynamic Libraries
- Dylib names and paths
- Version information
- Timestamps
- Load command types

#### Code Signing
- Signing status
- Certificate information
- Entitlements
- Code directory details

## Usage

### Basic Usage

```python
from redb.extractors.macho_extractors import MachOFeaturesExtractor

# Create extractor
extractor = MachOFeaturesExtractor(
    filepath="/path/to/macho/binary",
    log=logger
)

# Extract data
features = extractor.extract()

# Export to databases
extractor.export_data()
```

### Testing

Use the provided test script to verify functionality:

```bash
python test_macho_extractors.py /path/to/macho/binary
```

## Implementation Details

### Universal Binary Support

The extractors handle Universal/FAT binaries by:
1. Detecting FAT binary format
2. Extracting individual architectures
3. Providing unified interface for both single and multi-arch binaries
4. Supporting architecture-specific extraction

### Error Handling

- Graceful handling of malformed binaries
- Comprehensive logging for debugging
- Fallback mechanisms for missing data
- Exception handling for corrupted files

### Performance Considerations

- Lazy parsing of MachO structures
- Efficient memory usage for large binaries
- Cached property access for repeated queries
- Optimized data extraction patterns

## Integration

### Database Exporters

The extractors support both Elasticsearch and ClickHouse exporters:

- **Elasticsearch**: JSON document storage with full-text search
- **ClickHouse**: Columnar storage for analytical queries

### Schema Compatibility

All extractors follow the established schema patterns:
- Consistent field naming
- Proper data types
- Timestamp handling
- Hash field inclusion

## Future Enhancements

Potential areas for improvement:

1. **Additional Extractors**
   - MachO resources extraction
   - Symbol table analysis
   - Relocation information
   - Thread state analysis

2. **Enhanced Analysis**
   - Malware detection patterns
   - Behavioral analysis
   - Similarity hashing
   - YARA rule integration

3. **Performance Optimizations**
   - Parallel processing for multi-arch binaries
   - Streaming data processing
   - Memory-mapped file access

## Dependencies

- `machofile` library (included in `docs/`)
- Standard Python libraries (hashlib, datetime, etc.)
- RedB framework components

## Contributing

When adding new MachO extractors:

1. Follow the established pattern in existing extractors
2. Add appropriate dataclasses to `dataclasses.py`
3. Update the enum tags in `enum.py`
4. Include comprehensive error handling
5. Add tests for new functionality
6. Update this documentation

## Troubleshooting

### Common Issues

1. **Import Errors**: Ensure `machofile` library is accessible
2. **Memory Issues**: Large Universal binaries may require significant memory
3. **Corrupted Files**: Malformed MachO files may cause parsing errors
4. **Architecture Mismatch**: Some features may not be available for all architectures

### Debugging

Enable debug logging to see detailed extraction process:

```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

## License

This implementation follows the same license as the main RedB project.