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README.md

@@ -13,84 +13,7 @@ tags:
 - cross-platform
 size_categories:
 - 10K<n<100K
-pretty_name: 'Binary-30K: A Large-Scale Multi-Platform Binary Dataset'
-dataset_info:
-  features:
-  - name: file_id
-    dtype: string
-  - name: file_path
-    dtype: string
-  - name: file_name
-    dtype: string
-  - name: sha256
-    dtype: string
-  - name: md5
-    dtype: string
-  - name: file_size
-    dtype: int64
-  - name: platform
-    dtype: string
-  - name: os_family
-    dtype: string
-  - name: os_version
-    dtype: string
-  - name: distribution
-    dtype: string
-  - name: file_format
-    dtype: string
-  - name: architecture
-    dtype: string
-  - name: binary_type
-    dtype: string
-  - name: is_stripped
-    dtype: bool
-  - name: is_packed
-    dtype: bool
-  - name: is_signed
-    dtype: bool
-  - name: sections
-    struct:
-    - name: name
-      list: string
-    - name: size
-      list: int64
-    - name: entropy
-      list: float32
-  - name: num_sections
-    dtype: int32
-  - name: code_size
-    dtype: int64
-  - name: data_size
-    dtype: int64
-  - name: imports
-    list: string
-  - name: num_imports
-    dtype: int32
-  - name: exports
-    list: string
-  - name: num_exports
-    dtype: int32
-  - name: entropy
-    dtype: float32
-  - name: tokens
-    list: int32
-  - name: token_count
-    dtype: int32
-  - name: compression_ratio
-    dtype: float32
-  - name: unique_tokens
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 49491992359
-    num_examples: 38395
-  download_size: 21623507474
-  dataset_size: 49491992359
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+pretty_name: "Binary-30K: A Large-Scale Multi-Platform Binary Dataset"
 ---
 
 # Dataset Card for Binary-30K
@@ -124,13 +47,13 @@ configs:
 
 ### Dataset Summary
 
-Binary-30K is a comprehensive, multi-platform binary executable dataset designed for machine learning research in binary analysis, malware detection, and program understanding. The dataset contains **30,745 records** representing **22,278 unique binary executables** totaling **12.90 GB**, collected from diverse sources including Linux distributions, Windows operating systems, and the SOREL-20M malware dataset.
+Binary-30K is a comprehensive, multi-platform binary executable dataset designed for machine learning research in binary analysis, malware detection, and program understanding. The dataset contains **38,467 records** representing **~30,000 unique binary executables** totaling **~33.41 GB**, collected from diverse sources including Linux distributions, Windows operating systems, SOREL-20M malware dataset, and Malware Bazaar collection.
 
-**Note on Duplicates:** The dataset includes 30,745 total records but 22,278 unique SHA256 hashes. The duplicates (8,467 records) are primarily due to:
+**Note on Duplicates:** The dataset includes 38,467 total records representing ~30,000 unique SHA256 hashes. Among the benign binaries, there are approximately 8,467 duplicate records primarily due to:
 - **BusyBox binaries** (~1,827 records): Single multi-call binary with different command names (e.g., `ls`, `cp`, `mv` are hardlinks to the same BusyBox binary)
 - **Hardlinked system utilities**: Multiple names pointing to identical binaries across different Linux distributions
 
-This structure reflects real-world binary collections where utilities share implementations, and is valuable for studying binary deduplication and identifying multi-purpose executables.
+This structure reflects real-world binary collections where utilities share implementations, and is valuable for studying binary deduplication and identifying multi-purpose executables. The malware samples from SOREL-20M and Malware Bazaar are deduplicated and contribute unique binaries to the dataset.
 
 Each binary in the dataset has been pre-processed with:
 - **Pre-computed BPE tokenization** using the `mjbommar/glaurung-binary-tokenizer-001` tokenizer
@@ -140,19 +63,23 @@ Each binary in the dataset has been pre-processed with:
 - **Binary analysis** via LIEF library (ELF/PE parsing)
 
 The dataset is stratified across:
-- **Linux binaries** (50.8%): Alpine 3.18/3.19, Debian 11 (Bullseye)/12 (Bookworm), Ubuntu 20.04/22.04/24.04, BusyBox 1.37.0
-- **Windows binaries** (48.0%): Windows 8 Pro, Windows 10, Windows 11, Windows Update Catalog
-- **Malware samples** (1.2%): From SOREL-20M dataset
+- **Linux binaries** (47.2%): Alpine 3.18/3.19, Debian 11 (Bullseye)/12 (Bookworm), Ubuntu 20.04/22.04/24.04, BusyBox 1.37.0, plus Linux malware from Malware Bazaar
+- **Windows binaries** (44.5%): Windows 8 Pro, Windows 10, Windows 11, Windows Update Catalog, plus Windows malware from Malware Bazaar
+- **macOS binaries** (1.5%): macOS malware from Malware Bazaar (x86-64, ARM64, Universal binaries)
+- **Android binaries** (0.6%): Android malware APKs from Malware Bazaar
+- **Other/Diverse formats** (6.2%): Scripts, archives, and diverse formats from SOREL-20M and Malware Bazaar
 
-This dataset enables research in malware detection, architecture recognition, function boundary detection, compiler identification, binary similarity analysis, and cross-platform binary understanding.
+**Malware Representation**: The dataset includes 8,089 malware samples (21.0% of dataset) from SOREL-20M (367 samples) and Malware Bazaar (7,722 samples), providing strong class balance for malware detection research across Linux, Windows, macOS, and Android platforms.
+
+This dataset enables research in cross-platform malware detection, architecture recognition, function boundary detection, compiler identification, binary similarity analysis, mobile malware analysis, and multi-platform binary understanding.
 
 ### Supported Tasks
 
 **Binary Malware Detection**
-- Task: Binary classification (benign vs malicious)
+- Task: Binary classification (benign vs malicious) and cross-platform malware detection
 - Metrics: Accuracy, Precision, Recall, F1-score, AUC-ROC
 - Suggested Models: Transformer-based sequence models, CNN-based models
-- Use Case: Detect malicious executables using token sequences and metadata features
+- Use Case: Detect malicious executables across Linux, Windows, macOS, and Android platforms using token sequences and metadata features. Strong class balance (21% malware) enables effective training.
 
 **Architecture Recognition**
 - Task: Multi-class classification (x86, x86-64, ARM, ARM64, etc.)
@@ -315,9 +242,16 @@ test = train_test['test']        # ~4,627 samples (15%)
 ```
 
 **Distribution Statistics:**
-- Linux binaries: ~15,659 (51%)
-- Windows binaries: ~14,815 (48%)
-- Malware samples: ~367 (1%)
+- Linux binaries: ~18,165 (47.2%)
+- Windows binaries: ~17,125 (44.5%)
+- macOS binaries: ~568 (1.5%)
+- Android binaries: ~242 (0.6%)
+- Other/Diverse: ~2,367 (6.2%)
+
+**Malware Representation:**
+- Total malware samples: ~8,089 (21.0%)
+- Sources: SOREL-20M (367) + Malware Bazaar (7,722)
+- Cross-platform: Linux, Windows, macOS, Android
 
 ## Dataset Creation
 
@@ -337,17 +271,21 @@ This dataset provides:
 5. **Open access**: CC-BY-4.0 license with public availability
 
 The dataset enables research in:
-- Cross-platform malware detection
-- Architecture-agnostic binary analysis
-- Transfer learning between platforms
+- Cross-platform malware detection across Linux, Windows, macOS, and Android
+- Architecture-agnostic binary analysis including exotic architectures (MIPS, RISC-V, ARCompact, m68k)
+- Transfer learning between platforms and architectures
 - Tokenization-based binary understanding
 - Large-scale binary similarity analysis
+- Mobile malware detection (Android APKs)
+- IoT and embedded systems malware analysis
+
+Binary-30K is one of the few publicly available datasets with comprehensive cross-platform malware coverage at scale, including mobile platforms and exotic architectures, with strong class balance (21% malware) suitable for effective malware detection research.
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-**Linux Binaries (51% of dataset)**
+**Linux Binaries (47.2% of dataset)**
 
 Collected from official package repositories:
 - **Alpine Linux 3.18** and **3.19**: Lightweight distribution, musl libc-based static binaries
@@ -357,7 +295,7 @@ Collected from official package repositories:
 
 Binaries extracted from .deb packages and Alpine APK packages using standard package management tools.
 
-**Windows Binaries (48% of dataset)**
+**Windows Binaries (44.5% of dataset)**
 
 Collected from multiple Windows versions to capture compiler evolution:
 - **Windows 8 Pro (x64)**: System binaries and common applications
@@ -367,9 +305,11 @@ Collected from multiple Windows versions to capture compiler evolution:
 
 Binaries extracted from official Microsoft sources using update catalog and system file extraction.
 
-**Malware Samples (1% of dataset)**
+**Malware Samples (21% of dataset)**
+
+Drawn from two sources - SOREL-20M and Malware Bazaar:
 
-Drawn from SOREL-20M dataset:
+**SOREL-20M subset (367 samples)**:
 - **SOREL-20M**: Sophos-ReversingLabs 20 million sample malware dataset
   - Source: [https://github.com/sophos/SOREL-20M](https://github.com/sophos/SOREL-20M)
   - License: [SOREL-20M License Agreement](https://github.com/sophos/SOREL-20M/blob/main/LICENSE)
@@ -379,6 +319,28 @@ Drawn from SOREL-20M dataset:
 
 All samples handled in isolated environment following malware analysis best practices.
 
+**Malware Bazaar Samples (20% of dataset)**
+
+Strategically sampled from Malware Bazaar collection using Platform-First Stratified Sampling:
+- **Source**: [https://bazaar.abuse.ch/](https://bazaar.abuse.ch/)
+- **Sampling strategy**: Platform-First Stratified Sampling approach
+  - ALL macOS samples (568): Fills critical platform gap, includes x86-64, ARM64 (Apple Silicon), and Universal binaries
+  - ALL Android samples (242): Enables mobile malware research, APK format with ARM/ARM64 native libraries
+  - Windows samples (2,356): Stratified by file size for diversity (small scripts, medium tools, large packed binaries)
+  - Linux samples (2,556): Stratified by architecture, includes exotic architectures (MIPS, RISC-V, ARCompact, m68k, SH, PowerPC)
+  - Other formats (2,000): Diverse file types including scripts, archives, and obfuscated payloads
+- **Total**: 7,722 samples selected from 20,499 available (37.7% sampling rate)
+- **Selection criteria**: Maximize platform and architecture diversity to reach 30,000 unique binaries
+- **License**: Research use only, proper attribution required
+- **Sampling methodology**: Detailed documentation available at [SAMPLING_METHODOLOGY.md](https://github.com/mjbommar/binary-bpe-paper/blob/master/paper/dataset-paper/SAMPLING_METHODOLOGY.md)
+
+The Malware Bazaar samples provide:
+1. **Cross-platform malware detection**: Supports Linux, Windows, macOS, and Android malware analysis
+2. **Mobile malware research**: 242 Android APKs with ARM/ARM64 architectures
+3. **macOS malware analysis**: 568 samples including x86-64, ARM64 (Apple Silicon), and Universal binaries
+4. **Exotic architecture coverage**: 1,000+ samples with MIPS, RISC-V, ARCompact, m68k, SH, PowerPC enabling IoT/embedded malware research
+5. **Platform and architecture diversity**: Stratified sampling ensures representation across malware types, platforms, and architectures
+
 #### Source Language Producers
 
 The binaries were originally compiled from source code written by:
@@ -469,14 +431,19 @@ The dataset contains compiled binary executables. Potential sensitive informatio
 ### Discussion of Biases
 
 **Platform Bias:**
-- Linux (51%) and Windows (48%) are balanced, but represent only two major platforms
-- MacOS, mobile platforms (iOS/Android), and embedded systems not represented
-- This may limit generalization to other platforms
+- Linux (47.2%) and Windows (44.5%) are the dominant platforms
+- **macOS represented** (1.5%, 568 samples) including x86-64 and ARM64 (Apple Silicon) binaries
+- **Android represented** (0.6%, 242 samples) enabling mobile malware research
+- iOS not represented (future work)
+- Embedded systems represented via exotic Linux architectures (MIPS, RISC-V, ARCompact, m68k, SH, PowerPC)
 
 **Architecture Bias:**
-- Dominated by x86-64 architecture
-- Limited ARM samples despite ARM's growing importance (mobile, IoT, Apple Silicon)
-- Other architectures (MIPS, RISC-V) minimally represented
+- x86-64 is dominant but dataset includes diverse architectures:
+  - **ARM64**: macOS (Apple Silicon) and Android (64-bit) samples included
+  - **ARM**: Android (32-bit) samples included
+  - **Exotic architectures**: MIPS, RISC-V, ARCompact, m68k, SH, PowerPC from Malware Bazaar Linux samples (~1,000 samples)
+- Suitable for cross-architecture research across common and exotic architectures
+- IoT/embedded device architectures represented, enabling specialized malware detection research
 
 **Temporal Bias:**
 - Windows samples span Windows 8-11 (2012-2023)
@@ -489,9 +456,14 @@ The dataset contains compiled binary executables. Potential sensitive informatio
 - May not capture distribution-specific toolchain differences
 
 **Malware Bias:**
-- Malware samples only 1% of dataset (class imbalance)
-- Malware from SOREL-20M (2020 collection) may not represent current threats
-- Geographic/language bias in malware samples from original SOREL-20M
+- Malware samples represent 21% of dataset (strong class balance for classification tasks)
+- **Cross-platform malware** representation: Linux, Windows, macOS, Android
+- Malware from two sources:
+  - SOREL-20M (2020): 367 Windows PE samples
+  - Malware Bazaar (2020-2025): 7,722 samples spanning multiple years, platforms, and malware families
+- **Platform-stratified sampling** ensures diverse malware families and attack vectors across all major platforms
+- Geographic/language bias may exist due to collection sources
+- **Exotic architecture malware** (MIPS, RISC-V, ARCompact, m68k, SH, PowerPC) enables IoT/embedded malware detection research
 
 **Size Bias:**
 - File sizes vary widely; very large binaries may be underrepresented
@@ -576,13 +548,23 @@ This dataset contains binary executables from multiple sources, each subject to
    - Users are responsible for compliance with Microsoft terms and applicable laws in their jurisdiction
    - Consider consulting legal counsel for commercial applications
 
-3. **Malware Samples**: From SOREL-20M dataset (Sophos-ReversingLabs)
+3. **SOREL-20M Malware Samples**: From SOREL-20M dataset (Sophos-ReversingLabs)
    - Source: [SOREL-20M GitHub Repository](https://github.com/sophos/SOREL-20M)
    - License: [SOREL-20M License Agreement](https://github.com/sophos/SOREL-20M/blob/main/LICENSE)
    - Users must comply with SOREL-20M's terms of use
    - No copyright claimed on malware samples themselves
    - Malware samples should only be used in secure, isolated research environments
 
+4. **Malware Bazaar Samples**: From Malware Bazaar (abuse.ch)
+   - Source: [https://bazaar.abuse.ch/](https://bazaar.abuse.ch/)
+   - License: Research use only, proper attribution required
+   - 7,722 samples strategically selected via Platform-First Stratified Sampling
+   - Users must comply with Malware Bazaar's terms of use
+   - No copyright claimed on malware samples themselves
+   - Malware samples should only be used in secure, isolated research environments
+   - Attribution: Must acknowledge Malware Bazaar (abuse.ch) as source
+   - See [SAMPLING_METHODOLOGY.md](https://github.com/mjbommar/binary-bpe-paper/blob/master/paper/dataset-paper/SAMPLING_METHODOLOGY.md) for sampling details
+
 **Recommendations for Users:**
 - **Academic/Research Use**: Generally covered under fair use/fair dealing in most jurisdictions, but verify compliance with institutional policies
 - **Commercial Use**: Consult legal counsel regarding Microsoft binary licenses and other proprietary software