M. V. Panduranga Rao

48 papers B 2C 3Misc 10Journal 15Unranked 18
YearRankTypeTitle / Venue / Authors
2026 Misc conf
COMSNETS
Manan Patel, Sreyash Mohanty, M. V. Panduranga Rao
2025 J jnl
CoRR
Ahmik Virani, Devraj, Anirudh Suresh, Lei Zhang, M. V. Panduranga Rao
2025 Misc conf
ICDCN
Anoop Kumar Pandey, Bheemarjuna Reddy Tamma, M. V. Panduranga Rao
2025 Misc conf
COMSNETS
Anoop Kumar Pandey, Bheemarjuna Reddy Tamma, M. V. Panduranga Rao
2025 J jnl
Simul.
Yenda Ramesh, M. V. Panduranga Rao
2024 Misc conf
COMSNETS
Soumitri Kadambi, M. V. Panduranga Rao
2024 conf
ICCS (6)
Anubhav Srivastava, M. V. Panduranga Rao
2023 C conf
SEKE
Krishn Vishwas Kher, Ishan Joshi, Bharat Chandra Mukkavalli, Lei Zhang, M. V. Panduranga Rao
2023 conf
AIAI (1)
Thamilselvam B, Subrahmanyam Kalyanasundaram, M. V. Panduranga Rao
2023 Misc conf
ICDCN
Anoop Kumar Pandey, Anubhav Srivastava, Shuhul Handoo, Bheemarjuna Reddy Tamma, M. V. Panduranga Rao
2023 J jnl
CoRR
Pranav K. Nayak, Krishn Vishwas Kher, Bharat Chandra Mukkavalli, M. V. Panduranga Rao, Lei Zhang
2023 Misc conf
SAC
Thamilselvam B, Yenda Ramesh, Subrahmanyam Kalyanasundaram, M. V. Panduranga Rao
2022 conf
FORMATS
Surya Sai Teja Desu, Anubhav Srivastava, M. V. Panduranga Rao
2022 J jnl
Quantum Inf. Process.
Krishna V. Palem, Duc Hung Pham, M. V. Panduranga Rao
2022 conf
ICAART (1)
Yenda Ramesh, M. V. Panduranga Rao
2021 conf
ICCS (6)
Surya Sai Teja Desu, P. K. Srijith, M. V. Panduranga Rao, Naveen Sivadasan
2021 J jnl
CoRR
Surya Sai Teja Desu, P. K. Srijith, M. V. Panduranga Rao, Naveen Sivadasan
2021 conf
ANTS
B. Anvesh, ABS Phaneendra, M. Sai Anuraag, J. Sai Nishith, Anubhav Srivastava, A. Antony Franklin, M. V. Panduranga Rao, K. Dhanush, Devendra Mishra, Nixon Patel
2021 conf
ANNSIM
Yenda Ramesh, M. V. Panduranga Rao
2021 Misc conf
COMSNETS
Thamilselvam B, Subrahmanyam Kalyanasundaram, M. V. Panduranga Rao
2021 conf
ANTS
Anubhav Srivastava, Devendra Mishra, Madhuri Annavazzala, A. Antony Franklin, Nixon Patel, M. V. Panduranga Rao
2021 conf
SBMF
Thamilselvam B, Subrahmanyam Kalyanasundaram, Shubham Parmar, M. V. Panduranga Rao
2021 conf
KES-AMSTA
Rajesh Kumar Pandey, M. V. Panduranga Rao
2020 J jnl
CoRR
Duc Hung Pham, Krishna V. Palem, M. V. Panduranga Rao
2020 B conf
PRIMA
Ramesh Yenda, M. V. Panduranga Rao
2020 Misc conf
TASE
Shiraj Arora, M. V. Panduranga Rao
2019 Misc conf
COMSNETS
Thamilselvam B, Subrahmanyam Kalyanasundaram, M. V. Panduranga Rao
2019 B conf
PRIMA
Yenda Ramesh, Nikhil Anand, M. V. Panduranga Rao
2019 conf
SPIN
Shiraj Arora, M. V. Panduranga Rao
2019 Misc conf
COMSNETS
Yenda Ramesh, Nikhil Anand, M. V. Panduranga Rao
2019 J jnl
CoRR
Shiraj Arora, M. V. Panduranga Rao
2018 conf
COMPLEX NETWORKS (1)
Shiraj Arora, Abhishek Jain, Yenda Ramesh, M. V. Panduranga Rao
2017 conf
ICSH
Radhiya Arsekar, Durga Keerthi Mandarapu, M. V. Panduranga Rao
2017 J jnl
CoRR
Shiraj Arora, M. V. Panduranga Rao
2016 conf
ISoLA (1)
Shiraj Arora, M. V. Panduranga Rao
2015 conf
AINTEC
Shiraj Arora, Ankit Rathor, M. V. Panduranga Rao
2014 J jnl
CoRR
M. V. Panduranga Rao, K. Chandrashekar Shet
2014 conf
INCoS
Sudarshan S., Kayathi Rohith, K. P. Sai Krishna, M. V. Panduranga Rao
2009 J jnl
CLEI Electron. J.
M. V. Panduranga Rao, K. Chandrashekar Shet
2008 conf
AINA Workshops
M. V. Panduranga Rao, K. Chandrashekar Shet, R. Balakrishna, K. Roopa
2008 J jnl
Int. J. Found. Comput. Sci.
M. V. Panduranga Rao
2008 J jnl
Theor. Comput. Sci.
M. V. Panduranga Rao
2008 J jnl
J. Autom. Lang. Comb.
M. V. Panduranga Rao, V. Vinay
2007 C conf
TAMC
M. V. Panduranga Rao
2007 J jnl
CoRR
M. V. Panduranga Rao
2006 C conf
TAMC
M. V. Panduranga Rao
2005 conf
ACiD
M. V. Panduranga Rao, V. Vinay
2004 J jnl
CoRR
M. V. Panduranga Rao
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.