Carlos Oberdan Rolim

17 papers B 2C 1Misc 2Journal 5Unranked 6
YearRankTypeTitle / Venue / Authors
2019 J jnl
Sensors
Luis Augusto Silva, Valderi Reis Quietinho Leithardt, Carlos Oberdan Rolim, Gabriel Villarrubia-González, Cláudio F. R. Geyer, Jorge Sá Silva
2018 Misc conf
SAC
Antonio Rodrigo Delepiane de Vit, César A. M. Marcon, Raul Ceretta Nunes, Thais Webber, Gustavo Sanchez, Carlos Oberdan Rolim
2018 J jnl
J. Ubiquitous Syst. Pervasive Networks
Valderi R. Q. Leithardt, Luiz Henrique Andrade Correia, Guilherme A. Borges, Anubis G. M. Rossetto, Carlos Oberdan Rolim, Cláudio F. R. Geyer, Jorge M. Sá Silva
2016 J jnl
Comput. Networks
Carlos Oberdan Rolim, Anubis Graciela de Moraes Rossetto, Valderi R. Q. Leithardt, Guilherme A. Borges, Cláudio F. R. Geyer, Tatiana F. M. dos Santos, Adriano M. Souza
2016
Carlos Oberdan Rolim
2015 B conf
AINA
Anubis Graciela de Moraes Rossetto, Carlos Oberdan Rolim, Valderi R. Q. Leithardt, Guilherme A. Borges, Cláudio Fernando Resin Geyer, Luciana Arantes, Pierre Sens
2015 C conf
ISCC
Carlos Oberdan Rolim, Anubis Graciela de Moraes Rossetto, Valderi R. Q. Leithardt, Guilherme A. Borges, Cláudio F. R. Geyer, Tatiana F. M. dos Santos, Adriano M. Souza
2015 conf
ICEIS (2)
Carlos Oberdan Rolim, Anubis Graciela de Moraes Rossetto, Valderi R. Q. Leithardt, Guilherme A. Borges, Tatiana F. M. dos Santos, Adriano M. Souza, Cláudio Fernando Resin Geyer
2014 conf
HEALTHINF
Anubis Graciela de Moraes Rossetto, Cláudio Fernando Resin Geyer, Carlos Oberdan Rolim, Valderi R. Q. Leithardt, Luciana Arantes
2014 conf
ARE/AVSA@AAMAS
Carlos Oberdan Rolim, Anubis Graciela de Moraes Rossetto, Valderi R. Q. Leithardt, Guilherme A. Borges, Tatiana F. M. dos Santos, Adriano M. Souza, Cláudio F. R. Geyer
2014 J jnl
J. Ubiquitous Syst. Pervasive Networks
Valderi R. Q. Leithardt, David Nunes, Anubis Graciela de Moraes Rossetto, Carlos Oberdan Rolim, Cláudio F. R. Geyer, Jorge Sá Silva
2013 conf
Conf. Computing Frontiers
Carlos Oberdan Rolim, Cláudio F. R. Geyer
2012 conf
CLEI Selected Papers
Felipe Weber Fehlberg, Carlos Oberdan Rolim, Valderi R. Q. Leithardt, Cláudio Fernando Resin Geyer, Luciano Cavalheiro da Silva, Anubis Graciela de Moraes Rossetto
2012 Misc conf
ICNC
Valderi R. Q. Leithardt, Carlos Oberdan Rolim, A. Rosseto, Cláudio Fernando Resin Geyer, Mario A. R. Dantas, Jorge Sá Silva, David Nunes
2011 J jnl
Int. J. High Perform. Syst. Archit.
Anubis Graciela de Moraes Rossetto, Carlos Oberdan Rolim, Valderi R. Q. Leithardt, Mario A. R. Dantas, Cláudio F. R. Geyer
2010 conf
eTELEMED
Carlos Oberdan Rolim, Fernando Luiz Koch, Carlos Becker Westphall, Jorge Werner, Armando Fracalossi, Giovanni Schmitt Salvador
2006 B conf
CBMS
Carlos Oberdan Rolim, Fernando Luiz Koch, Marcos Dias de Assunção, Carlos Becker Westphall
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.