Duplicate from mjbommar/binary-30k

Co-authored-by: Michael Bommarito <mjbommar@users.noreply.huggingface.co>

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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+*.bmp filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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*

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