README.md

---
language:
- en
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-classification
tags:
- binary-analysis
- malware-detection
- cybersecurity
- cross-platform
- tokenized
- stratified-splits
---

# Binary-30K: Cross-Platform Binary Dataset with Stratified Splits

[Paper](https://huggingface.co/papers/2511.22095) | [Code](https://github.com/mjbommar/binary-dataset-paper)

**🔗 Original Dataset (no splits):** [`mjbommar/binary-30k-tokenized`](https://huggingface.co/datasets/mjbommar/binary-30k-tokenized)

This is the **stratified train/validation/test split version** of the Binary-30K dataset, containing **29,793 unique cross-platform binaries** with pre-computed tokenization. This version provides standardized splits for reproducible machine learning research.

## 🎯 Key Features

- ✅ **Stratified 70/15/15 splits** maintaining class balance across all sets
- ✅ **4-dimensional stratification** across malware/platform/format/architecture
- ✅ **26.9% malware balance** preserved in all splits (±0.1%)
- ✅ **Deterministic splits** (seed=42) for reproducible research
- ✅ **Ready for ML** - no manual splitting required
- ✅ **Pre-computed BPE tokenization** for transformer models

## 📊 Dataset Splits

| Split | Samples | Malware | Benign | Malware % |
|-------|---------|---------|--------|-----------|
| **Train** | 20,849 | 5,613 | 15,236 | 26.92% |
| **Validation** | 4,463 | 1,200 | 3,263 | 26.89% |
| **Test** | 4,481 | 1,210 | 3,271 | 27.00% |
| **Total** | 29,793 | 8,023 | 21,770 | 26.93% |

### Stratification Strategy

Splits maintain proportional representation across:
- ✅ **Malware vs. Benign** (26.9% malware in each split)
- ✅ **Platform** (Windows, Linux, macOS, Android, Other)
- ✅ **File Format** (PE, ELF, Mach-O, APK)
- ✅ **Architecture Groups** (common: x86/ARM vs. exotic: MIPS/RISC-V/PowerPC)

**19 unique strata identified** with proportional representation maintained across all splits.

## 🚀 Quick Start

```python
from datasets import load_dataset

# Load dataset with splits
dataset = load_dataset("mjbommar/binary-30k")

train_ds = dataset["train"]        # 20,849 samples
val_ds = dataset["validation"]      # 4,463 samples
test_ds = dataset["test"]           # 4,481 samples

# Access pre-computed tokens
sample = train_ds[0]
print(f"Platform: {sample['platform']}")
print(f"Malware: {sample['is_malware']}")
print(f"Tokens: {len(sample['tokens'])} tokens")
```

### Example: Malware Classification

```python
from datasets import load_dataset
from transformers import Trainer, TrainingArguments

# Load data
dataset = load_dataset("mjbommar/binary-30k")

# Tokens are pre-computed - just truncate
def prepare_example(example):
    return {
        "input_ids": example["tokens"][:512],
        "labels": int(example["is_malware"])
    }

# Train on standard splits
train_ds = dataset["train"].map(prepare_example)
val_ds = dataset["validation"].map(prepare_example)

# Train your model...
```

### Example: Cross-Platform Transfer Learning

```python
# Train on Windows, test on Linux
train_windows = dataset["train"].filter(lambda x: x["platform"] == "windows")
test_linux = dataset["test"].filter(lambda x: x["platform"] == "linux")

print(f"Windows training samples: {len(train_windows)}")
print(f"Linux test samples: {len(test_linux)}")
# Evaluate cross-platform generalization...
```

## 📦 Dataset Composition

**Platform Distribution:**
- **Windows**: 57.3% (17,239 samples) - PE32/PE32+ executables and DLLs
- **Linux**: 28.4% (8,452 samples) - ELF32/ELF64 from 9 distributions
- **macOS**: 1.9% (568 samples) - x86-64, ARM64, Universal binaries
- **Android**: 0.6% (164 samples) - APKs with native ARM libraries
- **Other**: 11.8% (3,370 samples) - Diverse formats and installers

**Architecture Diversity:**
- **Common**: x86-64 (56.4%), x86 (11.1%), ARM64 (5.9%), ARM (9.4%)
- **Exotic**: MIPS (2.3%), PowerPC (1.3%), RISC-V (0.1%), m68k, SuperH, ARCompact, SPARC, S/390

**Malware Sources:**
- **SOREL-20M**: 365 Windows PE malware samples (2017-2019)
- **Malware Bazaar**: 7,658 cross-platform malware samples (2020-2024)
  - Platform-first stratified sampling
  - ALL available macOS malware (560 samples)
  - ALL available Android malware (164 samples)
  - Balanced Windows/Linux with size stratification

## 📋 Data Structure

Each record contains **31 fields** organized into seven categories:

**Identification** (6 fields):
- `file_id`, `file_path`, `file_name`, `sha256`, `md5`, `file_size`

**Platform/Source** (5 fields):
- `platform`, `os_family`, `os_version`, `distribution`, `is_malware`

**File Characteristics** (6 fields):
- `file_format`, `architecture`, `binary_type`, `is_stripped`, `is_packed`, `is_signed`

**Structural Analysis** (4 fields + sections):
- `num_sections`, `code_size`, `data_size`, `sections[]`

**Dependencies** (4 fields + imports/exports):
- `num_imports`, `num_exports`, `imports[]`, `exports[]`

**Complexity** (1 field):
- `entropy` (Shannon entropy 0-8 scale)

**Pre-computed Tokenization** (4 fields):
- `tokens[]`, `token_count`, `compression_ratio`, `unique_tokens`

**Parser Diagnostics** (2 fields):
- `parse_status`, `parse_warnings[]`

### Pre-computed Tokenization

All binaries are tokenized using **BPE tokenization** ([`mjbommar/binary-tokenizer-001-64k`](https://huggingface.co/mjbommar/binary-tokenizer-001-64k)):
- **Average tokens per binary**: ~15,000
- **Compression ratio**: ~4.2 bytes/token
- **Vocabulary**: 64K tokens
- **Ready for transformers**: BERT, GPT, T5, etc.

## 🎓 Supported Research Tasks

1.  **Malware Detection**: Binary classification with balanced classes (26.9% malware)
2.  **Cross-Platform Analysis**: Transfer learning across Windows/Linux/macOS/Android
3.  **Architecture-Invariant Detection**: Generalization to exotic architectures (IoT/embedded)
4.  **Mobile Malware Research**: Dedicated Android and macOS malware samples
5.  **Binary Similarity**: Embedding learning for similar binary detection
6.  **Format-Agnostic Analysis**: Multi-format models (PE/ELF/Mach-O/APK)

## 📊 Comparison with Other Datasets

| Dataset | Size | Platforms | Architectures | Malware | Pre-tokenized | Splits |
|---------|------|-----------|---------------|---------|---------------|--------|\
| **Binary-30K** | 30K | Win+Linux+macOS+Android | 15+ (incl. exotic) | 26.9% | ✅ | ✅ |
| SOREL-20M | 20M | Windows only | x86/x64 | 100% | ❌ | ❌ |
| EMBER | 1.1M | Windows only | x86/x64 | 50% | ❌ (features) | ✅ |
| Assemblage | 1.1M | Windows+Linux | x86/x64 | 0% (benign) | ❌ | ❌ |

## 🔍 Stratification Verification

**Split Distribution Verification:**

**TRAIN (20,849 samples):**
- Malware: 5,613 (26.92%)
- Top platforms: Windows (12,065), Linux (5,915), Other (1,200)
- Top formats: PE (12,018), ELF (5,915), Unknown (1,195)

**VALIDATION (4,463 samples):**
- Malware: 1,200 (26.89%)
- Top platforms: Windows (2,584), Linux (1,266), Other (256)
- Top formats: PE (2,574), ELF (1,266), Unknown (255)

**TEST (4,481 samples):**
- Malware: 1,210 (27.00%)
- Top platforms: Windows (2,590), Linux (1,271), Other (259)
- Top formats: PE (2,577), ELF (1,271), Unknown (258)

**Statistical Tests:** Chi-square tests confirm no significant deviation from proportional representation (p > 0.05 for all dimensions).

## 🔄 Reproducibility

**Split Generation:**
- **Seed**: 42 (for reproducibility)
- **Method**: Stratified sampling with composite keys
- **Date**: November 15, 2025
- **Tool**: [`binary-dataset-paper`](https://github.com/mjbommar/binary-dataset-paper)

All splits are **deterministic and reproducible**. Using the same seed will always produce identical splits.

## 📚 Data Sources

**Linux Binaries:** Alpine 3.18/3.19, Debian 11-12, Ubuntu 20.04/22.04/24.04, Fedora 39-40, CentOS Stream 9, Arch Linux, Kali Linux 2024.1, Parrot OS 6.0, BusyBox 1.37.0

**Windows Binaries:** Windows 8 Pro, Windows 10 21H2/22H2, Windows 11 23H2, Windows Update Catalog

**Malware Samples:**
- SOREL-20M dataset (Sophos-ReversingLabs, 2020)
- Malware Bazaar (abuse.ch, 2020-2024) with platform-first stratified sampling

## ⚠️ Important Considerations

**Limitations:**
- Static analysis only (no dynamic/runtime behavior)
- Some binaries cannot be parsed by LIEF
- Many binaries have stripped debug symbols
- Very large binaries produce extended token sequences
- iOS/iPadOS binaries not included
- Uneven representation of exotic architectures

**Usage Notes:**
- **Malware samples require secure, isolated research environments**
- Windows binaries subject to Microsoft licensing terms
- Fair use application depends on jurisdiction
- Splits are standardized but users may create custom splits for specific research needs

## 📄 License and Attribution

**Dataset Compilation:** CC-BY-4.0 license by Michael J. Bommarito II

**Component Licenses:**
- Linux binaries: Various open-source licenses (GPL, LGPL, MIT, BSD, Apache)
- Windows binaries: Subject to Microsoft software licenses
- SOREL-20M samples: Follow [SOREL-20M License Agreement](https://github.com/sophos-ai/SOREL-20M)
- Malware Bazaar samples: Research use only, attribution required to [abuse.ch](https://abuse.ch/)

**Malware samples** are included for research purposes only. Users must comply with applicable laws and regulations when working with malware samples.

## 📖 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{bommarito2025binary30k,
  title={Binary-30K: A Cross-Platform, Multi-Architecture Binary Dataset with Stratified Splits},
  author={Bommarito, Michael J., II},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/mjbommar/binary-30k}
}
```

## 🔗 Related Resources

- **Original dataset (no splits)**: [`mjbommar/binary-30k-tokenized`](https://huggingface.co/datasets/mjbommar/binary-30k-tokenized)
- **Tokenizer**: [`mjbommar/binary-tokenizer-001-64k`](https://huggingface.co/mjbommar/binary-tokenizer-001-64k)
- **Paper**: [Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection](https://huggingface.co/papers/2511.22095)
- **Code & Documentation**: [github.com/mjbommar/binary-dataset-paper](https://github.com/mjbommar/binary-dataset-paper)
- **Technical Documentation**: See [DATASET_SPLITS.md](https://github.com/mjbommar/binary-dataset-paper/blob/master/DATASET_SPLITS.md) for detailed stratification methodology

## 📞 Contact

**Author:** Michael J. Bommarito II
**Email:** michael.bommarito@gmail.com

## 🔄 Updates

- **2025-11-15**: Initial release with stratified train/val/test splits (70/15/15)

---

*Last Updated: November 15, 2025*