James T. Rayfield

33 papers A* 1A 11B 3Misc 1Journal 14Unranked 3
YearRankTypeTitle / Venue / Authors
2026 A* conf
AAAI
Dhaval Patel, Nianjun Zhou, Shuxin Lin, James T. Rayfield, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula
2026 J jnl
CoRR
Fearghal O'Donncha, Nianjun Zhou, Natalia Martinez, James T. Rayfield, Fenno F. Terry Heath III, Abigail Langbridge, Roman Vaculín
2025 J jnl
CoRR
Dhaval Patel, Shuxin Lin, James T. Rayfield, Nianjun Zhou, Roman Vaculín, Natalia Martinez Gil, Fearghal O'Donncha, Jayant Kalagnanam
2025 conf
EMNLP (Industry Track)
James T. Rayfield, Shuxin Lin, Nianjun Zhou, Dhaval Patel
2024 B conf
IEEE Big Data
Abigail Langbridge, Fearghal O'Donncha, James T. Rayfield, Bradley Eck
2021 J jnl
CoRR
Kanthi K. Sarpatwar, Karthik Nandakumar, Nalini K. Ratha, James T. Rayfield, Karthikeyan Shanmugam, Sharath Pankanti, Roman Vaculín
2020 conf
CVPR Workshops
Kanthi K. Sarpatwar, Nalini K. Ratha, Karthik Nandakumar, Karthikeyan Shanmugam, James T. Rayfield, Sharath Pankanti, Roman Vaculín
2019 J jnl
IBM J. Res. Dev.
Venkat S. K. Balagurusamy, Cyril Cabral, Srikumar Coomaraswamy, Emmanuel Delamarche, Donna N. Dillenberger, Gero Dittmann, Daniel J. Friedman, Onur Gökçe, Nigel Hinds, Jens Jelitto, Andreas Kind, Ashwin Dhinesh Kumar, Frank Libsch, Joseph W. Ligman, Seiji Munetoh, Chandra Narayanaswami, Abhilash Narendra, Arun Paidimarri, Miguel Ángel Prada-Delgado, James T. Rayfield, Chitra K. Subramanian, Roman Vaculín
2017 A conf
ICWS
Avraham Leff, James T. Rayfield
2016 A conf
ICWS
Avraham Leff, James T. Rayfield
2015 A conf
ICWS
Avraham Leff, James T. Rayfield
2013 A conf
ICWS
Avraham Leff, James T. Rayfield, Ravi B. Konuru, Raj Balasubramanian
2012 A conf
ICWS
Avraham Leff, James T. Rayfield, Tom Gissel, Ben Parees
2010 A conf
ICWS
Avraham Leff, James T. Rayfield
2010 A conf
ICWS
Avraham Leff, James T. Rayfield
2009 J jnl
Adv. Comput.
Avraham Leff, James T. Rayfield
2008 J jnl
IEEE Internet Comput.
Avraham Leff, James T. Rayfield
2008 conf
IEEE SCC (1)
Avraham Leff, James T. Rayfield
2007 B conf
VL/HCC
Avraham Leff, James T. Rayfield
2007 A conf
OOPSLA
Avraham Leff, James T. Rayfield
2006 J jnl
IEEE Internet Comput.
Avraham Leff, James T. Rayfield
2006 J jnl
Adv. Comput.
Avraham Leff, James T. Rayfield
2005 J jnl
Concurr. Pract. Exp.
Avraham Leff, James T. Rayfield
2004 A conf
Middleware
Avraham Leff, James T. Rayfield
2003 A conf
ICDCS
Avraham Leff, James T. Rayfield
2003 J jnl
IEEE Internet Comput.
Avraham Leff, James T. Rayfield, Daniel M. Dias
2002 J jnl
IBM Syst. J.
Daniel M. Dias, Stewart L. Palmer, James T. Rayfield, Hidayatullah Shaikh, T. K. Sreeram
2001 J jnl
Adv. Comput.
Avraham Leff, John Prokopek, James T. Rayfield, Ignacio Silva-Lepe
2001 B conf
EDOC
Avraham Leff, James T. Rayfield
2000 A conf
Middleware
Brian T. Bennett, Bill Hahm, Avraham Leff, Thomas A. Mikalsen, Kevin Rasmus, James T. Rayfield, Isabelle Rouvellou
1988 Misc conf
ICASSP
James T. Rayfield, Harvey F. Silverman
1988 J jnl
Computer
James T. Rayfield, Harvey F. Silverman
1985 J jnl
IBM J. Res. Dev.
James T. Rayfield, Harvey F. Silverman
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.