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README.md

@@ -42,8 +42,8 @@ pretty_name: "Binary-30K: A Large-Scale Multi-Platform Binary Dataset"
 
 - **Homepage:** [https://michaelbommarito.com/](https://michaelbommarito.com/)
 - **Repository:** [https://github.com/mjbommar/binary-bpe-paper](https://github.com/mjbommar/binary-bpe-paper)
-- **Paper:** Binary-30K: A Large-Scale Multi-Platform Binary Dataset for Machine Learning Research
-- **Point of Contact:** michael@bommaritollc.com
+- **Paper:** [arXiv:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX) (to be updated)
+- **Point of Contact:** michael.bommarito@gmail.com
 
 ### Dataset Summary
 
@@ -63,8 +63,8 @@ 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, Debian 11/12, Ubuntu 20.04/22.04, BusyBox
-- **Windows binaries** (48.0%): Windows 8, 10, 11, Windows Update Catalog
+- **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
 
 This dataset enables research in malware detection, architecture recognition, function boundary detection, compiler identification, binary similarity analysis, and cross-platform binary understanding.
@@ -273,19 +273,19 @@ The dataset enables research in:
 **Linux Binaries (51% of dataset)**
 
 Collected from official package repositories:
-- **Alpine Linux 3.18**: Lightweight distribution, static binaries
-- **Debian 11 (Bullseye)** and **Debian 12 (Bookworm)**: Stable releases
-- **Ubuntu 20.04 LTS** and **Ubuntu 22.04 LTS**: Long-term support releases
-- **BusyBox**: Embedded systems binaries
+- **Alpine Linux 3.18** and **3.19**: Lightweight distribution, musl libc-based static binaries
+- **Debian 11 (Bullseye)** and **Debian 12 (Bookworm)**: Stable releases with glibc
+- **Ubuntu 20.04 LTS**, **22.04 LTS**, and **24.04 LTS**: Long-term support releases
+- **BusyBox 1.37.0** (glibc): Embedded systems multi-call binary
 
 Binaries extracted from .deb packages and Alpine APK packages using standard package management tools.
 
 **Windows Binaries (48% of dataset)**
 
 Collected from multiple Windows versions to capture compiler evolution:
-- **Windows 8**: System binaries and common applications
-- **Windows 10**: Multiple builds covering several years
-- **Windows 11**: Latest OS release
+- **Windows 8 Pro (x64)**: System binaries and common applications
+- **Windows 10 (x64)**: Multiple builds covering several years
+- **Windows 11 (x64)**: Latest OS release
 - **Windows Update Catalog**: System updates and drivers
 
 Binaries extracted from official Microsoft sources using update catalog and system file extraction.
@@ -294,6 +294,9 @@ Binaries extracted from official Microsoft sources using update catalog and syst
 
 Drawn from SOREL-20M dataset:
 - **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)
+  - Citation: Harang, R. & Rudd, E. M. (2020). SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
 - Subset selection: Representative samples across malware families
 - Deduplication: SHA-256 based to avoid duplicates
 
@@ -415,7 +418,7 @@ The dataset contains compiled binary executables. Potential sensitive informatio
 
 **Size Bias:**
 - File sizes vary widely; very large binaries may be underrepresented
-- Tokenization truncation may affect very large files (>1MB)
+- Very large binaries (>10MB) may result in long token sequences requiring more computational resources
 
 **Researchers should:**
 - Be aware of these biases when drawing conclusions
@@ -429,17 +432,18 @@ The dataset contains compiled binary executables. Potential sensitive informatio
 1. **Static Analysis Only**: Dataset contains binaries without runtime behavior
    - No dynamic analysis features (API calls, network activity, file operations)
 
-2. **Tokenization Context Window**: Very large binaries truncated during tokenization
-   - May lose information from files >1-2MB after tokenization
-
-3. **Parsing Failures**: Some binaries cannot be parsed by LIEF
+2. **Parsing Failures**: Some binaries cannot be parsed by LIEF
    - Corrupted files, unusual formats, or packers may result in missing metadata
 
-4. **Architecture Detection**: Based on headers, may be incorrect for obfuscated binaries
+3. **Architecture Detection**: Based on headers, may be incorrect for obfuscated binaries
 
-5. **Stripped Binaries**: Many binaries have debug symbols removed
+4. **Stripped Binaries**: Many binaries have debug symbols removed
    - Limits metadata extraction (fewer function names, no source line info)
 
+5. **Token Sequence Length**: Very large binaries (>10MB) produce long token sequences
+   - May require memory-efficient processing techniques or chunking strategies
+   - Full binary content is preserved in tokenization without truncation
+
 **Usage Limitations:**
 
 1. **Malware Handling**: Users must follow security best practices
@@ -450,8 +454,9 @@ The dataset contains compiled binary executables. Potential sensitive informatio
    - Users should verify compliance with local laws
 
 3. **Computational Requirements**: Full dataset requires significant resources
-   - 12.90 GB raw data + 15-20 GB tokenized dataset
-   - Token sequences can be very long (10K+ tokens)
+   - 12.90 GB raw data + tokenized dataset with complete metadata
+   - Token sequences vary from hundreds to hundreds of thousands of tokens (no truncation applied)
+   - Large binaries may require substantial memory for processing full token sequences
 
 4. **Not a Benchmark**: No standardized train/test splits or evaluation protocol
    - Users should define splits and protocols appropriate for their research
@@ -463,37 +468,52 @@ The dataset contains compiled binary executables. Potential sensitive informatio
 This dataset was curated by:
 
 **Michael J. Bommarito II**
-- Email: michael@bommaritollc.com
-- Affiliation: Bommarito Consulting, LLC
+- Email: michael.bommarito@gmail.com
+- Website: [https://michaelbommarito.com/](https://michaelbommarito.com/)
 
-With contributions from collaborators in the binary analysis and machine learning research communities.
+All dataset curation, organization, metadata extraction, and documentation by Michael J. Bommarito II.
 
 ### Licensing Information
 
-This dataset is released under **Creative Commons Attribution 4.0 International (CC-BY-4.0)** license.
+This dataset compilation is released under **Creative Commons Attribution 4.0 International (CC-BY-4.0)** license by Michael J. Bommarito II.
 
-**Summary:**
+**Dataset Compilation License (CC-BY-4.0):**
 - ✅ Share: Copy and redistribute in any medium or format
 - ✅ Adapt: Remix, transform, and build upon the material for any purpose
 - ✅ Commercial use allowed
 - ⚠️ Attribution required: Must give appropriate credit and indicate if changes were made
 
-**Component Licenses:**
+**Important: Component-Specific Licenses and Legal Considerations**
 
-1. **Linux Binaries**: Various open source licenses (GPL, LGPL, MIT, BSD, Apache)
+This dataset contains binary executables from multiple sources, each subject to their own licenses and legal frameworks. Users are responsible for ensuring their use complies with applicable laws and licenses in their jurisdiction.
+
+1. **Linux Binaries**: Various open source licenses (GPL, LGPL, MIT, BSD, Apache, etc.)
    - Original software licenses remain in effect for the binaries themselves
-   - Dataset compilation and metadata under CC-BY-4.0
+   - Dataset compilation, organization, and metadata under CC-BY-4.0
+   - Users should review individual package licenses as needed
 
 2. **Windows Binaries**: Microsoft software licenses
-   - Research and educational use covered under fair use
-   - Users responsible for compliance with Microsoft terms
+   - These binaries are subject to Microsoft's software license terms
+   - Research and educational use may be covered under fair use/fair dealing doctrines depending on jurisdiction
+   - Commercial use may require additional licensing from Microsoft
+   - 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)
-   - SOREL-20M released for research purposes
+   - 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
+
+**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
+- **International Use**: Fair use laws vary by jurisdiction; ensure compliance with local regulations
+- **Malware Handling**: Follow cybersecurity best practices and institutional review board requirements
 
 **Attribution:**
-When using this dataset, please cite the associated paper (see Citation Information below).
+When using this dataset, please cite the associated paper (see Citation Information below) and acknowledge Michael J. Bommarito II as the dataset curator.
 
 ### Citation Information
 
@@ -532,8 +552,8 @@ dataset = load_dataset("mjbommar/binary-30k-tokenized")
 ```
 
 **Direct Download:**
-- HTTP: https://michaelbommarito.com/resources/glaurung/data/glaurung-model-binaries-20251028.tar.gz
-- S3: s3://michaelbommarito.com/resources/glaurung/data/glaurung-model-binaries-20251028.tar.gz
+- HTTPS: https://s3.amazonaws.com/michaelbommarito.com/resources/glaurung/data/glaurung-model-binaries-20251028.tar.gz
+- S3 URI: s3://michaelbommarito.com/resources/glaurung/data/glaurung-model-binaries-20251028.tar.gz
 - Size: 12.90 GB (compressed)
 
 ### Contributions
@@ -547,7 +567,7 @@ Special thanks to:
 
 **Contributing:**
 - Report issues or suggest improvements via GitHub: https://github.com/mjbommar/binary-bpe-paper
-- Contact: michael@bommaritollc.com
+- Contact: michael.bommarito@gmail.com
 
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