James A. Shackleford

21 papers A* 1B 4Misc 1Journal 10Unranked 5
YearRankTypeTitle / Venue / Authors
2024 conf
MWSCAS
Shadi Matinizadeh, Arghavan Mohammadhassani, Noah Pacik-Nelson, Ioannis Polykretis, Abhishek Kumar Mishra, James A. Shackleford, Nagarajan Kandasamy, Eric M. Gallo, Anup Das
2024 J jnl
CoRR
Shadi Matinizadeh, Noah Pacik-Nelson, Ioannis Polykretis, Krupa Tishbi, Suman Kumar, M. Lakshmi Varshika, Arghavan Mohammadhassani, Abhishek Kumar Mishra, Nagarajan Kandasamy, James A. Shackleford, Eric M. Gallo, Anup Das
2024 J jnl
CoRR
Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, Gregory C. Sharp
2024 conf
ICONS
Shadi Matinizadeh, Arghavan Mohammadhassani, Noah Pacik-Nelson, Ioannis Polykretis, Krupa Tishbi, Suman Kumar, M. Lakshmi Varshika, Abhishek Kumar Mishra, Nagarajan Kandasamy, James A. Shackleford, Eric M. Gallo, Anup Das
2022 J jnl
ACM Trans. Embed. Comput. Syst.
Shihao Song, Harry Chong, Adarsha Balaji, Anup Das, James A. Shackleford, Nagarajan Kandasamy
2021 J jnl
CoRR
Shihao Song, Harry Chong, Adarsha Balaji, Anup Das, James A. Shackleford, Nagarajan Kandasamy
2021 J jnl
IEEE Embed. Syst. Lett.
Adarsha Balaji, Shihao Song, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, James A. Shackleford, Nagarajan Kandasamy, Francky Catthoor
2021 B conf
Image Processing
Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, Gregory C. Sharp
2021 conf
ICONS
Adarsha Balaji, Shihao Song, Twisha Titirsha, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, James A. Shackleford, Nagarajan Kandasamy, Francky Catthoor
2021 J jnl
CoRR
Adarsha Balaji, Shihao Song, Twisha Titirsha, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, James A. Shackleford, Nagarajan Kandasamy, Francky Catthoor
2020 J jnl
CoRR
Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, Gregory C. Sharp
2020 B conf
LCTES
Shihao Song, Adarsha Balaji, Anup Das, Nagarajan Kandasamy, James A. Shackleford
2020 J jnl
CoRR
Shihao Song, Adarsha Balaji, Anup Das, Nagarajan Kandasamy, James A. Shackleford
2020 J jnl
CoRR
Adarsha Balaji, Shihao Song, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, James A. Shackleford, Nagarajan Kandasamy, Francky Catthoor
2019 Misc conf
ICASSP
Brian C. Hosler, Owen Mayer, Belhassen Bayar, Xinwei Zhao, Chen Chen, James A. Shackleford, Matthew Christopher Stamm
2019 J jnl
IEEE Access
Brian C. Hosler, Xinwei Zhao, Owen Mayer, Chen Chen, James A. Shackleford, Matthew C. Stamm
2018 A* conf
CVPR
Pingge Jiang, James A. Shackleford
2018 B conf
Image Processing
Rajath Elias Soans, James A. Shackleford
2017 B conf
Image Processing
Pingge Jiang, James A. Shackleford
2015 conf
BIH
Pingge Jiang, James A. Shackleford
2012 conf
MICCAI (2)
James A. Shackleford, Qi Yang, Ana M. Lourenço, Nadya Shusharina, Nagarajan Kandasamy, Gregory C. Sharp
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.